Analytics Engineer (Python & Product Analytics)

🏢 Summary: Analytics Engineer role focused on building scalable healthcare data products and analytics-ready datasets for PBM and care navigation operations. The position involves developing dbt-based data models, optimizing cloud data platforms, implementing data quality frameworks, and enabling AI-assisted analytics capabilities. Candidates will collaborate cross-functionally to deliver reliable, maintainable, and auditable data solutions in a modern analytics environment. 🗂️ Requirements: 3+ years experience in analytics engineering, data modeling, data warehousing or related field, Strong experience with dbt, Advanced SQL proficiency, Experience developing data transformations and workflows using Python, Understanding of dimensional modeling and scalable data architecture, Experience building production-grade data models and data quality tests, Experience with cloud data warehouses, Experience orchestrating and monitoring data pipelines, Experience working in cloud environments, Familiarity with Git and CI/CD workflows, Knowledge of testing and deployment workflows, Strong problem-solving and analytical skills, Ability to collaborate with technical and non-technical stakeholders 📃 Skills: dbt, SQL, Python, Redshift, Snowflake, BigQuery, Airflow, Dagster, AWS, Git, CI/CD, MCP, Claude 🏢 Description: ABOUT THE ROLE: We are seeking an Analytics Engineer to join our Analytics Platform team to design, build and maintain scalable data products that power our Pharmacy Benefit Management (PBM) and Care Navigation organizations. In this role, you will be responsible for transforming complex healthcare data into trusted, analytics ready datasets that support operational reporting, client insights, financial analysis and strategic decision-making. You will partner closely with Analytics, Data Engineering, Clinical, Client Success, Product and Operations teams to build reliable and scalable data models within our data foundry. This role is ideal for someone who is passionate about analytics engineering, has strong dbt expertise, enjoys solving complex healthcare data challenges and thinks beyond individual requests to build reusable and maintainable systems. You will play a key role in shaping the future of our data platform by developing high-quality data products, establishing engineering best practices, and helping the organization evolve toward AI-enabled and self-service analytics capabilities. WHAT YOU'LL DO: • Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space • Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence • Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models • Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs • Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability • Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability • Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability • Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability • Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation • Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows WHO YOU ARE: • 3+ years of experience in analytics engineering, data modeling, data warehousing or a related field • Strong experience with dbt and modern analytics engineering best practices (required) • Advanced SQL proficiency and experience developing data transformations and workflows using python • Strong understanding of dimensional modeling, medallion architecture, semantic modeling and scalable data architecture principles • Experience building and maintaining production-grade data models, data quality tests, and documentation • Experience working with cloud data warehouses such as Amazon Redshift, Snowflake, or BigQuery • Experience orchestrating and monitoring data pipelines using tools such as Apache Airflow, Dagster etc. • Experience working within cloud environments, preferably AWS • Familiarity with software engineering practices including Git, CI/CD, pull request reviews, testing, and deployment workflows • Interest or experience in leveraging AI-enabled development workflows and analytics tooling, including experience with or willingness to learn technologies such as Claude, Claude Skills, Model Context Protocol (MCP) and AI assisted engineering practices • Strong problem-solving and analytical skills with a focus on building scalable, maintainable data solutions • Effective communication skills and the ability to collaborate with both technical and non-technical stakeholders COMPENSATION: $100,000 - $140,000 annually, in addition to bonus and equity. Compensation offered will be determined by geographic location, experience, and qualifications.

🏢 Summary: Analytics Engineer role focused on building scalable healthcare data products and analytics-ready datasets for PBM and care navigation systems. The position involves developing dbt-based data models, optimizing cloud data warehouses, implementing data quality frameworks, and enabling AI-assisted analytics capabilities. Candidates will collaborate across analytics, engineering, product, and operations teams to support reporting, operational analysis, and self-service business intelligence. 🗂️ Requirements: 3+ years experience in analytics engineering, data modeling, or data warehousing, Strong dbt expertise, Advanced SQL proficiency, Experience with Python data transformations and workflows, Knowledge of dimensional modeling and scalable data architecture, Experience with production-grade data models and data quality testing, Experience with cloud data warehouses, Experience orchestrating data pipelines, Experience working in AWS environments, Familiarity with Git and CI/CD workflows, Knowledge of testing and deployment workflows, Strong analytical and problem-solving skills, Ability to collaborate with technical and non-technical stakeholders 📃 Skills: dbt, SQL, Python, Redshift, Snowflake, BigQuery, Airflow, Dagster, AWS, Git, CI/CD, MCP, Claude 🏢 Description: ABOUT THE ROLE: We are seeking an Analytics Engineer to join our Analytics Platform team to design, build and maintain scalable data products that power our Pharmacy Benefit Management (PBM) and Care Navigation organizations. In this role, you will be responsible for transforming complex healthcare data into trusted, analytics ready datasets that support operational reporting, client insights, financial analysis and strategic decision-making. You will partner closely with Analytics, Data Engineering, Clinical, Client Success, Product and Operations teams to build reliable and scalable data models within our data foundry. This role is ideal for someone who is passionate about analytics engineering, has strong dbt expertise, enjoys solving complex healthcare data challenges and thinks beyond individual requests to build reusable and maintainable systems. You will play a key role in shaping the future of our data platform by developing high-quality data products, establishing engineering best practices, and helping the organization evolve toward AI-enabled and self-service analytics capabilities. WHAT YOU'LL DO: Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence. Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models. Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs. Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability. Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability. Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability. Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability. Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation. Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows. WHO YOU ARE: 3+ years of experience in analytics engineering, data modeling, data warehousing or a related field. Strong experience with dbt and modern analytics engineering best practices (required). Advanced SQL proficiency and experience developing data transformations and workflows using python. Strong understanding of dimensional modeling, medallion architecture, semantic modeling and scalable data architecture principles. Experience building and maintaining production-grade data models, data quality tests, and documentation. Experience working with cloud data warehouses such as Amazon Redshift, Snowflake, or BigQuery. Experience orchestrating and monitoring data pipelines using tools such as Apache Airflow., Dagster etc. Experience working within cloud environments, preferably AWS. Familiarity with software engineering practices including Git, CI/CD, pull request reviews, testing, and deployment workflows. Interest or experience in leveraging AI-enabled development workflows and analytics tooling, including experience with or willingness to learn technologies such as claude, claude skills, Model Context Protocol (MCP) and AI assisted engineering practices. Strong problem-solving and analytical skills with a focus on building scalable, maintainable data solutions. Effective communication skills and the ability to collaborate with both technical and non-technical stakeholders. COMPENSATION: $100,000 - $140,000 annually, in addition to bonus and equity Compensation offered will be determined by geographic location, experience, and qualifications.CYBERSECURITY AWARENESS NOTICE In response to ongoing and industry-wide fraudulent recruitment activities (i.e., job scams), Rightway wants to inform potential candidates that we will only contact them from the @rightwayhealthcare.com email domain. We will never ask for bank details or deposits of any kind as a condition of employment. ABOUT RIGHTWAY: Rightway is on a mission to harmonize healthcare for everyone, everywhere. Our products guide patients to the best care and medications by inserting clinicians and pharmacists into a patient's care journey through a modern, mobile app. Rightway is a front door to healthcare, giving patients the tools they need along with on-demand access to Rightway health guides, human experts that answer their questions and manage the frustrating parts of healthcare for them. Since its founding in 2017, Rightway has raised over $200mm from investors including Khosla Ventures, Thrive Capital, and Tiger Global. We're headquartered in New York City, with satellite offices in Denver and Dallas. Our clients rely on us to transform the healthcare experience, improve outcomes for their teams, and decrease their healthcare costs. HOW WE LIVE OUR VALUES TO OUR TEAMMATES: We're seeking those with passion for healthcare and relentless devotion to our goal. We need team members that embody our core values: 1) We are human, firstOur humanity binds us together. We bring the same empathetic approach to every individual we engage with, whether it be our members, our clients, or each other. We are all worthy of respect and understanding and we engage in our interactions with care and intention. We honor our stories. We listen to—and hear—each other, we celebrate our differences and similarities, we are present for each other, and we strive for mutual understanding. 2) We redefine what is possibleWe always look beyond the obstacles in front of us to imagine new solutions. We approach our work with inspiration from other industries, other leaders, and other challenges. We use ingenuity and resourcefulness when faced with tough problems. 3) We debate then commitWe believe that a spirit of open discourse is part of a healthy culture. We understand and appreciate different perspectives and we challenge our assumptions. When working toward a decision or a new solution, we actively listen to one another, approach it with a "yes, and" mentality, and assume positive intent. Once a decision is made, we align and champion it as one team. 4) We cultivate gritChanging healthcare doesn't happen overnight. We reflect and learn from challenges and approach the future with a determination to strive for better. In the face of daunting situations, we value persistence. We embrace failure as a stepping stone to future success. On this journey, we seek to act with guts, resilience, initiative, and tenacity. 5) We seek to delightHealthcare is complicated and personal. We work tirelessly to meet the goals of our clients while also delivering the best experience to our members. We recognize that no matter the role or team, we each play a crucial part in our members' care and take that responsibility seriously. When faced with an obstacle, we are kind, respectful, and solution-oriented in our approach. We hold ourselves accountable to our clients and our members' success. Rightway is proud to be an Equal Opportunity Employer that believes in the strength of diverse thought, beliefs, backgrounds, and education. We foster an inclusive culture where differences are celebrated to drive the best business decisions possible. We do not discriminate on any basis protected by applicable law. All employment decisions are based on merit, qualifications, need, and performance.

🏢 Summary: Analytics Engineer role focused on building scalable healthcare data products and analytics-ready datasets for PBM and care navigation operations. The position emphasizes dbt-based data modeling, cloud data warehousing, automated data quality processes, and collaboration across analytics, engineering, and business teams. The role also supports AI-enabled analytics capabilities and self-service data platforms. 🗂️ Requirements: 3+ years experience in analytics engineering, data modeling, data warehousing or related field, Strong experience with dbt, Advanced SQL proficiency, Experience developing data transformations and workflows with Python, Understanding of dimensional modeling and scalable data architecture, Experience building production-grade data models and data quality tests, Experience with cloud data warehouses, Experience orchestrating and monitoring data pipelines, Experience working in cloud environments, Familiarity with Git and CI/CD workflows, Experience with testing and deployment workflows, Strong problem-solving and analytical skills, Ability to collaborate with technical and non-technical stakeholders 📃 Skills: dbt, SQL, Python, Redshift, Snowflake, BigQuery, Airflow, Dagster, AWS, Git, CI/CD, MCP, Claude 🏢 Description: ABOUT THE ROLE: We are seeking an Analytics Engineer to join our Analytics Platform team to design, build and maintain scalable data products that power our Pharmacy Benefit Management (PBM) and Care Navigation organizations. In this role, you will be responsible for transforming complex healthcare data into trusted, analytics ready datasets that support operational reporting, client insights, financial analysis and strategic decision-making. You will partner closely with Analytics, Data Engineering, Clinical, Client Success, Product and Operations teams to build reliable and scalable data models within our data foundry. This role is ideal for someone who is passionate about analytics engineering, has strong dbt expertise, enjoys solving complex healthcare data challenges and thinks beyond individual requests to build reusable and maintainable systems. You will play a key role in shaping the future of our data platform by developing high-quality data products, establishing engineering best practices, and helping the organization evolve toward AI-enabled and self-service analytics capabilities. WHAT YOU'LL DO: Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence. Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models. Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs. Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability. Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability. Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability. Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability. Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation. Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows. WHO YOU ARE: 3+ years of experience in analytics engineering, data modeling, data warehousing or a related field. Strong experience with dbt and modern analytics engineering best practices (required). Advanced SQL proficiency and experience developing data transformations and workflows using python. Strong understanding of dimensional modeling, medallion architecture, semantic modeling and scalable data architecture principles. Experience building and maintaining production-grade data models, data quality tests, and documentation. Experience working with cloud data warehouses such as Amazon Redshift, Snowflake, or BigQuery. Experience orchestrating and monitoring data pipelines using tools such as Apache Airflow., Dagster etc. Experience working within cloud environments, preferably AWS. Familiarity with software engineering practices including Git, CI/CD, pull request reviews, testing, and deployment workflows. Interest or experience in leveraging AI-enabled development workflows and analytics tooling, including experience with or willingness to learn technologies such as claude, claude skills, Model Context Protocol (MCP) and AI assisted engineering practices. Strong problem-solving and analytical skills with a focus on building scalable, maintainable data solutions. Effective communication skills and the ability to collaborate with both technical and non-technical stakeholders. COMPENSATION: $100,000 - $140,000 annually, in addition to bonus and equity Compensation offered will be determined by geographic location, experience, and qualifications.CYBERSECURITY AWARENESS NOTICE In response to ongoing and industry-wide fraudulent recruitment activities (i.e., job scams), Rightway wants to inform potential candidates that we will only contact them from the @rightwayhealthcare.com email domain. We will never ask for bank details or deposits of any kind as a condition of employment. ABOUT RIGHTWAY: Rightway is on a mission to harmonize healthcare for everyone, everywhere. Our products guide patients to the best care and medications by inserting clinicians and pharmacists into a patient's care journey through a modern, mobile app. Rightway is a front door to healthcare, giving patients the tools they need along with on-demand access to Rightway health guides, human experts that answer their questions and manage the frustrating parts of healthcare for them. Since its founding in 2017, Rightway has raised over $200mm from investors including Khosla Ventures, Thrive Capital, and Tiger Global. We're headquartered in New York City, with satellite offices in Denver and Dallas. Our clients rely on us to transform the healthcare experience, improve outcomes for their teams, and decrease their healthcare costs. HOW WE LIVE OUR VALUES TO OUR TEAMMATES: We're seeking those with passion for healthcare and relentless devotion to our goal. We need team members that embody our core values: 1) We are human, firstOur humanity binds us together. We bring the same empathetic approach to every individual we engage with, whether it be our members, our clients, or each other. We are all worthy of respect and understanding and we engage in our interactions with care and intention. We honor our stories. We listen to—and hear—each other, we celebrate our differences and similarities, we are present for each other, and we strive for mutual understanding. 2) We redefine what is possibleWe always look beyond the obstacles in front of us to imagine new solutions. We approach our work with inspiration from other industries, other leaders, and other challenges. We use ingenuity and resourcefulness when faced with tough problems. 3) We debate then commitWe believe that a spirit of open discourse is part of a healthy culture. We understand and appreciate different perspectives and we challenge our assumptions. When working toward a decision or a new solution, we actively listen to one another, approach it with a "yes, and" mentality, and assume positive intent. Once a decision is made, we align and champion it as one team. 4) We cultivate gritChanging healthcare doesn't happen overnight. We reflect and learn from challenges and approach the future with a determination to strive for better. In the face of daunting situations, we value persistence. We embrace failure as a stepping stone to future success. On this journey, we seek to act with guts, resilience, initiative, and tenacity. 5) We seek to delightHealthcare is complicated and personal. We work tirelessly to meet the goals of our clients while also delivering the best experience to our members. We recognize that no matter the role or team, we each play a crucial part in our members' care and take that responsibility seriously. When faced with an obstacle, we are kind, respectful, and solution-oriented in our approach. We hold ourselves accountable to our clients and our members' success. Rightway is proud to be an Equal Opportunity Employer that believes in the strength of diverse thought, beliefs, backgrounds, and education. We foster an inclusive culture where differences are celebrated to drive the best business decisions possible. We do not discriminate on any basis protected by applicable law. All employment decisions are based on merit, qualifications, need, and performance.

🏢 Summary: Analytics Engineer role focused on building scalable healthcare data products and analytics-ready models for PBM and care navigation operations using dbt, SQL, and cloud data platforms. The position involves developing reliable data pipelines, implementing data quality frameworks, and enabling self-service and AI-assisted analytics capabilities. Candidates will collaborate across technical and business teams to create maintainable, auditable, and high-performance data solutions. 🗂️ Requirements: 3+ years experience in analytics engineering, data modeling, data warehousing or related field, Strong experience with dbt, Advanced SQL proficiency, Experience developing data transformations and workflows using Python, Understanding of dimensional modeling and scalable data architecture, Experience building production-grade data models and data quality tests, Experience with cloud data warehouses, Experience orchestrating and monitoring data pipelines, Experience working in cloud environments, Familiarity with Git and CI/CD workflows, Knowledge of testing and deployment workflows, Strong problem-solving and analytical skills, Effective communication and collaboration skills 📃 Skills: dbt, SQL, Python, Redshift, Snowflake, BigQuery, Airflow, Dagster, AWS, Git, CI/CD, MCP, Claude 🏢 Description: ABOUT THE ROLE: We are seeking an Analytics Engineer to join our Analytics Platform team to design, build and maintain scalable data products that power our Pharmacy Benefit Management (PBM) and Care Navigation organizations. In this role, you will be responsible for transforming complex healthcare data into trusted, analytics ready datasets that support operational reporting, client insights, financial analysis and strategic decision-making. You will partner closely with Analytics, Data Engineering, Clinical, Client Success, Product and Operations teams to build reliable and scalable data models within our data foundry. This role is ideal for someone who is passionate about analytics engineering, has strong dbt expertise, enjoys solving complex healthcare data challenges and thinks beyond individual requests to build reusable and maintainable systems. You will play a key role in shaping the future of our data platform by developing high-quality data products, establishing engineering best practices, and helping the organization evolve toward AI-enabled and self-service analytics capabilities. WHAT YOU'LL DO: • Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space • Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence • Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models • Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs • Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability • Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability • Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability • Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability • Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation • Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows WHO YOU ARE: • 3+ years of experience in analytics engineering, data modeling, data warehousing or a related field • Strong experience with dbt and modern analytics engineering best practices (required) • Advanced SQL proficiency and experience developing data transformations and workflows using Python • Strong understanding of dimensional modeling, medallion architecture, semantic modeling and scalable data architecture principles • Experience building and maintaining production-grade data models, data quality tests, and documentation • Experience working with cloud data warehouses such as Amazon Redshift, Snowflake, or BigQuery • Experience orchestrating and monitoring data pipelines using tools such as Apache Airflow, Dagster etc. • Experience working within cloud environments, preferably AWS • Familiarity with software engineering practices including Git, CI/CD, pull request reviews, testing, and deployment workflows • Interest or experience in leveraging AI-enabled development workflows and analytics tooling, including experience with or willingness to learn technologies such as Claude, Claude Skills, Model Context Protocol (MCP) and AI assisted engineering practices • Strong problem-solving and analytical skills with a focus on building scalable, maintainable data solutions • Effective communication skills and the ability to collaborate with both technical and non-technical stakeholders COMPENSATION: $100,000 - $140,000 annually, in addition to bonus and equity. Compensation offered will be determined by geographic location, experience, and qualifications.

🏢 Summary: Opportunity for an experienced Python Data Engineer to build scalable data platforms and analytics systems for global financial institutions. The role focuses on designing cloud-based data pipelines, enhancing system reliability, and integrating modern AI tools into engineering workflows. You will collaborate across distributed teams to deliver high-quality solutions in enterprise data, risk analytics, and AI-driven metadata applications. 🗂️ Requirements: 4+ years of professional experience in software or data engineering using Python, Strong knowledge of data architecture, data modeling, and data warehousing, Ability to write complex SQL queries and perform advanced debugging, Experience building scalable, distributed data pipelines in cloud environments, Experience with AWS, Familiarity with event-driven architectures 📃 Skills: Python, SQL, AWS, Databricks, Spark, PySpark, Scala, Delta, Airflow, dbt, Kafka, Kubernetes, MongoDB, Grafana, Loki, Prometheus, OpenTelemetry 🏢 Description: We are partnering with top-tier global financial institutions to scale their core technology and data infrastructure. We are looking for an experienced and product-oriented Python Data Engineer to join our technology group. In this role, you will work at the intersection of cutting-edge technology and institutional finance. You will collaborate closely with data consumers, engineering teams, and business stakeholders to push the firm's technological capabilities forward. What You’ll Do: Core Responsibilities: Design, develop, and deliver high-quality, scalable Python-based solutions. Drive engineering excellence by ensuring system reliability, automating processes, and maintaining high operational standards. Actively experiment with and integrate modern AI coding tools (e.g., Copilot, Cursor) to streamline engineering workflows. Lead design discussions, mentor junior colleagues, and communicate proactively across a geo-distributed team. Depending on the specific project or team, your focus may include: Enterprise Data Platforms: Building cloud-based (AWS) pipelines for data ingestion, streaming, and cataloging. Risk & Portfolio Analytics Systems: Developing software for financial data ingress/egress, performance/exposure monitoring, and automated reconciliation. AI & Metadata Applications: Extending AI-powered semantic data access layers and internal data catalogs. What We’re Looking For: Experience: 4+ years of professional experience in software or data engineering, specifically using Python . Data Skills: Solid understanding of data architecture, modeling, and warehousing. Excellent debugging acumen and comfort writing complex SQL statements. Cloud & Architecture: Experience building scalable, distributed pipelines in a cloud environment (preferably AWS ). Familiarity with event-driven architectures. Mindset: Impact-oriented, proactive, self-starting learner who embraces engineering automation, navigates ambiguity well, and holds themselves to high ethical standards. Nice to Have: Big Data & Orchestration: Expertise in Databricks, Spark (PySpark/Scala), Delta Lake, and orchestration tools like Airflow, dbt, or Kafka. Infrastructure & Observability: Working knowledge of Kubernetes, MongoDB, and observability stacks (Grafana, Loki, Prometheus, OpenTelemetry). Offer: Competitive compensation (30k - 45k PLN) with flexible contracting options (B2B/UoP). Premium office location in the heart of Warsaw. Comprehensive private medical and dental care. Sports card and wellness benefits. Private life insurance

🏢 Summary: Python Software Engineer role focused on building and operating production-grade Python applications, APIs, and data pipelines in a governed data platform environment. The position involves transforming data science prototypes into deployable services, optimizing PySpark workloads, and collaborating with platform teams on Databricks and CI/CD practices. The offer includes flexible remote work, career development programs, certifications, and comprehensive benefits. 🗂️ Requirements: 3+ years of Python engineering experience, Experience with Python packaging, Knowledge of typing and clean architecture, Proficiency in error handling and performance-oriented development, Experience with Git workflows, Experience with automated testing, Experience with CI/CD, Expertise in Pandas, Expertise in NumPy, Experience with Parquet data formats, Experience building APIs or services, Experience with FastAPI, Flask or similar frameworks, Experience working in Databricks or containerized environments 📃 Skills: Python, Pandas, NumPy, Parquet, FastAPI, Flask, Databricks, Spark, PySpark, Git, CI/CD, APIs, SDLC, scikit-learn 🏢 Description: We are seeking a Python Software Engineer to join our Production Data & Model Services team. In this role, you will build and operate production-grade Python applications, transform data science prototypes into deployable services and collaborate with platform teams to deliver robust data pipelines and APIs. Responsibilities Build and run production-grade Python applications (APIs and batch jobs) with strong SDLC practices including code reviews, testing, CI/CD, observability and documentation Develop robust data pipelines (batch and near-real-time) reading and writing governed storage with Parquet/columnar formats and approved patterns Transform quant and data science prototypes into deployable packages/services (typed, modular, versioned) Expose scoring and analytics via APIs or scheduled jobs rather than notebook-only deliverables Collaborate with platform teams on Databricks/Spark connectivity Optimize PySpark workloads when needed Ensure release discipline through Git workflows, automated tests and code reviews Requirements 3+ years of strong Python engineering experience including packaging (wheels/pyproject), typing and clean architecture Proficiency in error handling and performance-oriented development Proven production SDLC background with Git workflows, automated tests and CI/CD Expertise in Pandas and NumPy in production pipelines Familiarity with data formats like Parquet and governed data access patterns Experience building and operating APIs/services using FastAPI, Flask or similar frameworks Competency working in governed platform environments such as Databricks or containerized dev platforms Nice to have Skills in scikit-learn for production feature and scoring pipelines, including reproducible transforms and model packaging/versioning Background in PySpark and distributed processing Knowledge of IDE-to-Databricks workflows such as Databricks Connect We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: The offer is for a Python Software Engineer to build and operate production-grade applications and data pipelines within a Production Data & Model Services team. You will transform data science prototypes into deployable services, expose analytics via APIs or scheduled jobs and collaborate with platform teams in governed environments. The role emphasizes strong SDLC practices, CI/CD, and scalable data processing with Python and Spark-based technologies. 🗂️ Requirements: 3+ years of Python engineering experience, Experience with Python packaging (wheels, pyproject), typing and clean architecture, Strong production SDLC experience with Git workflows, automated tests and CI/CD, Proficiency in error handling and performance-oriented development, Expertise in Pandas and NumPy in production pipelines, Experience with Parquet and governed data access patterns, Experience building and operating APIs/services with FastAPI, Flask or similar, Experience working in governed platforms such as Databricks or containerized environments 📃 Skills: Python, Pandas, NumPy, Parquet, FastAPI, Flask, PySpark, Databricks, Git, CI/CD, Spark, scikit-learn, GCP, Azure, AWS 🏢 Description: We are seeking a Python Software Engineer to join our Production Data & Model Services team. In this role, you will build and operate production-grade Python applications, transform data science prototypes into deployable services and collaborate with platform teams to deliver robust data pipelines and APIs. Responsibilities Build and run production-grade Python applications (APIs and batch jobs) with strong SDLC practices including code reviews, testing, CI/CD, observability and documentation Develop robust data pipelines (batch and near-real-time) reading and writing governed storage with Parquet/columnar formats and approved patterns Transform quant and data science prototypes into deployable packages/services (typed, modular, versioned) Expose scoring and analytics via APIs or scheduled jobs rather than notebook-only deliverables Collaborate with platform teams on Databricks/Spark connectivity Optimize PySpark workloads when needed Ensure release discipline through Git workflows, automated tests and code reviews Requirements 3+ years of strong Python engineering experience including packaging (wheels/pyproject), typing and clean architecture Proficiency in error handling and performance-oriented development Proven production SDLC background with Git workflows, automated tests and CI/CD Expertise in Pandas and NumPy in production pipelines Familiarity with data formats like Parquet and governed data access patterns Experience building and operating APIs/services using FastAPI, Flask or similar frameworks Competency working in governed platform environments such as Databricks or containerized dev platforms Nice to have Skills in scikit-learn for production feature and scoring pipelines, including reproducible transforms and model packaging/versioning Background in PySpark and distributed processing Knowledge of IDE-to-Databricks workflows such as Databricks Connect We offer We gather like-minded people: Engineering community of industry professionals Friendly team and enjoyable working environment Flexible schedule and opportunity to work remotely within Poland Chance to work abroad for up to 60 days annually Business-driven relocation opportunities We provide growth opportunities: Outstanding career roadmap Leadership development, career advising, soft skills, and well-being programs Certification (GCP, Azure, AWS) Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru English classes We cover it all: Stable income (Employment Contract or B2B) Participation in the Employee Stock Purchase Plan Benefits package (health insurance, multisport, shopping vouchers) Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more Referral bonuses Corporate, social and well-being events Please, note: The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview. We will reach out to selected candidates exclusively. EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

🏢 Summary: Full-time remote B2B role for a Software Engineer focused on Python backend development and implementation of Generative AI solutions for internal enterprise products. The position involves integrating LLM-based solutions via APIs, developing microservices, and collaborating with Data Scientists in Agile teams. Work is primarily backend-focused with exposure to Azure OpenAI, RAG concepts, and enterprise-scale environments. 🗂️ Requirements: Strong Python backend development experience, Experience implementing LLM solutions via APIs, Understanding of Generative AI architectures, Experience with enterprise-scale environments, Experience building and consuming APIs, Ability to work in regulated or structured environments, English proficiency at C1 or strong B2 📃 Skills: Python, GenAI, LLM, API, RAG, Azure, OpenAI, Microservices, Agile 🏢 Description: PROJECT DETAILS: Workload: Full-time Model of working: 100% Remote Contract: B2B, long-term Rate: 140-165 PLN/h nett + VAT About the Role We are looking for a Software Engineer to work on Generative AI–based internal products developed in the company, supporting internal employees. You will join a distributed, fully remote team working on one of two AI-related business streams. The focus of the role is on Python backend development combined with implementation of Generative AI solutions , closely collaborating with Data Scientists who prepare the models. The solutions you build will be used internally within the organization. Responsibilities Design, develop, and deploy backend solutions in Python Implement Generative AI models prepared by Data Scientists into production-ready applications Wrap AI models with application logic and expose them as ready-to-use internal solutions Work mainly with API-based integrations , including LLM usage via APIs Implement and support LLM-based solutions (classic LLM usage through APIs) Work with Retrieval-Augmented Generation (RAG) concepts, including knowledge bases for language models Develop and maintain APIs and microservices Ensure solutions meet enterprise-level quality, security, and governance standards Write clean, maintainable code with appropriate test coverage Collaborate within Agile teams (PM, PO, developers, QA, architects) Participate in the full lifecycle: Business analysis Development Hypercare Further development Technology & Architecture Context Backend and GenAI work is fully Python-based GenAI models are consumed and exposed via APIs Azure OpenAI is used as part of the Azure ecosystem Strong emphasis on correct and well-structured implementation of AI models , not on model training Approximate work split: 80% backend Python development 20% GenAI/LLM solution implementation Key Requirements Strong experience in Python backend development Experience implementing solutions using LLMs via API Understanding of Generative AI architectures and basic GenAI concepts Experience with enterprise-scale environments Experience in building and consuming APIs Ability to work in regulated or structured organizational environments English: C1 or strong B2 Nice to Have Experience in regulated industries Familiarity with RAG concepts (vector databases / knowledge bases) Experience working in large enterprise organizations

🏢 Summary: The offer is for an experienced Python Data Engineer to build scalable, cloud-based data and analytics solutions for global financial institutions. The role focuses on designing distributed data pipelines, enhancing system reliability, and developing financial and AI-driven data platforms. You will work with modern cloud and AI tools to advance enterprise data architecture and analytics capabilities. 🗂️ Requirements: 4+ years of professional experience in Python data or software engineering, Strong knowledge of data architecture, data modeling, and data warehousing, Advanced SQL skills and complex query writing, Experience building scalable distributed data pipelines, Hands-on experience with cloud environments (AWS preferred), Understanding of event-driven architectures, Experience with debugging and system reliability practices 📃 Skills: Python, SQL, AWS, Databricks, Spark, PySpark, Scala, Delta, Airflow, dbt, Kafka, Kubernetes, MongoDB, Grafana, Loki, Prometheus, OpenTelemetry 🏢 Description: We are partnering with top-tier global financial institutions to scale their core technology and data infrastructure. We are looking for an experienced and product-oriented Python Data Engineer to join our technology group. In this role, you will work at the intersection of cutting-edge technology and institutional finance. You will collaborate closely with data consumers, engineering teams, and business stakeholders to push the firm's technological capabilities forward. What You’ll Do: Core Responsibilities: Design, develop, and deliver high-quality, scalable Python-based solutions. Drive engineering excellence by ensuring system reliability, automating processes, and maintaining high operational standards. Actively experiment with and integrate modern AI coding tools (e.g., Copilot, Cursor) to streamline engineering workflows. Lead design discussions, mentor junior colleagues, and communicate proactively across a geo-distributed team. Depending on the specific project or team, your focus may include: Enterprise Data Platforms: Building cloud-based (AWS) pipelines for data ingestion, streaming, and cataloging. Risk & Portfolio Analytics Systems: Developing software for financial data ingress/egress, performance/exposure monitoring, and automated reconciliation. AI & Metadata Applications: Extending AI-powered semantic data access layers and internal data catalogs. What We’re Looking For Experience: 4+ years of professional experience in software or data engineering, specifically using Python . Data Skills: Solid understanding of data architecture, modeling, and warehousing. Excellent debugging acumen and comfort writing complex SQL statements. Cloud & Architecture: Experience building scalable, distributed pipelines in a cloud environment (preferably AWS ). Familiarity with event-driven architectures. Mindset: Impact-oriented, proactive, self-starting learner who embraces engineering automation, navigates ambiguity well, and holds themselves to high ethical standards. Nice to Have: Big Data & Orchestration: Expertise in Databricks, Spark (PySpark/Scala), Delta Lake, and orchestration tools like Airflow, dbt, or Kafka. Infrastructure & Observability: Working knowledge of Kubernetes, MongoDB, and observability stacks (Grafana, Loki, Prometheus, OpenTelemetry). Offer: Competitive compensation (30k - 45k PLN) with flexible contracting options (B2B/UoP). Premium office location in the heart of Warsaw. Comprehensive private medical and dental care. Sports card and wellness benefits. Private life insurance

🏢 Summary: Full-time remote B2B role for a Software Engineer focused on Python backend development and implementation of Generative AI solutions for internal enterprise products. The position involves integrating LLM-based models via APIs, building microservices, and deploying production-ready AI applications in an Azure-based environment. Work is primarily backend development with partial focus on GenAI/LLM integration in a distributed Agile team. 🗂️ Requirements: Strong experience in Python backend development, Experience implementing LLM solutions via APIs, Understanding of Generative AI architectures and concepts, Experience building and consuming APIs, Experience with enterprise-scale systems, Ability to work in regulated or structured environments, English proficiency at B2 or higher 📃 Skills: Python, GenAI, LLM, API, Azure, AzureOpenAI, RAG, Microservices, REST, VectorDB, Git, Agile 🏢 Description: PROJECT DETAILS: Workload: Full-time Model of working: 100% Remote Contract: B2B, long-term Rate: up to 145 PLN/h nett + VAT About the Role We are looking for a Software Engineer to work on Generative AI–based internal products developed in the company, supporting internal employees. You will join a distributed, fully remote team working on one of two AI-related business streams. The focus of the role is on Python backend development combined with implementation of Generative AI solutions , closely collaborating with Data Scientists who prepare the models. The solutions you build will be used internally within the organization. Responsibilities Design, develop, and deploy backend solutions in Python Implement Generative AI models prepared by Data Scientists into production-ready applications Wrap AI models with application logic and expose them as ready-to-use internal solutions Work mainly with API-based integrations , including LLM usage via APIs Implement and support LLM-based solutions (classic LLM usage through APIs) Work with Retrieval-Augmented Generation (RAG) concepts, including knowledge bases for language models Develop and maintain APIs and microservices Ensure solutions meet enterprise-level quality, security, and governance standards Write clean, maintainable code with appropriate test coverage Collaborate within Agile teams (PM, PO, developers, QA, architects) Participate in the full lifecycle: Business analysis Development Hypercare Further development Technology & Architecture Context Backend and GenAI work is fully Python-based GenAI models are consumed and exposed via APIs Azure OpenAI is used as part of the Azure ecosystem Strong emphasis on correct and well-structured implementation of AI models , not on model training Approximate work split: 80% backend Python development 20% GenAI/LLM solution implementation Key Requirements Strong experience in Python backend development Experience implementing solutions using LLMs via API Understanding of Generative AI architectures and basic GenAI concepts Experience with enterprise-scale environments Experience in building and consuming APIs Ability to work in regulated or structured organizational environments English: C1 or strong B2 Nice to Have Experience in regulated industries Familiarity with RAG concepts (vector databases / knowledge bases) Experience working in large enterprise organizations