Senior Data Engineer

Quick Facts

Senior Data Engineer role focused on building trusted, reusable datasets and company-wide metrics.

Description

Build and scale data models, pipelines, and metric systems that provide consistent, timely business insights. Champion data quality, governance, and reliability from prototyping through production and ongoing maintenance. Lead initiatives to standardize critical company metrics and mentor teams to deliver reusable data products.

Responsibilities

• Partner with Product and business stakeholders to translate high-impact questions into scalable data models, metrics, analyses, and technical solutions
• Work with business domain experts, Data Analytics, Data Science, and Engineering to build trusted foundational datasets aligned with business strategy and enabling self-service analytics
• Design, build, and scale data models and pipelines integrating multiple sources into accessible datasets with measurable quality and predictable SLA performance
• Own the end-to-end lifecycle of metrics, analytical models, and data products—from exploration and prototyping through production, adoption, and ongoing maintenance
• Use AI-assisted development to accelerate engineering productivity while maintaining standards for code, data quality, and maintainability
• Lead and influence data strategy across multiple teams and domains
• Expand access to trusted company metrics to enable faster, more consistent decision-making
• Establish, document, and promote data engineering best practices
• Mentor engineers with hands-on technical guidance to raise the organization’s technical bar

Requirements

• 8+ years of overall software engineering or data engineering experience
• 5+ years hands-on experience with data architecture, data modeling, data management, and metadata management
• Ability to structure and own ambiguous, high-impact problems end-to-end
• Deep expertise in SQL and experience designing scalable pipelines and transformations that operate reliably at scale
• Proven track record optimizing data models/schemas and processing workflows for performance, scalability, cost efficiency, and reliability
• Ability to influence technical direction and drive alignment across Engineering, Product, Data Science, Analytics, and business stakeholders
• Proficiency in at least one data engineering language: Python or Java
• Hands-on experience with Snowflake, Spark, Airflow, Hive
• Experience with relational and NoSQL stores and modeling approaches including logging, columnar, star/snowflake schemas, and dimensional modeling
• Familiarity with data governance frameworks, SDLC practices, and Agile methodologies
• Excellent written and verbal communication skills

Benefits

• Time off programs
• Medical, dental, vision
• Mental health support
• Paid parental leave
• Life and disability insurance
• 401(k)
• Employee stock purchasing program
• AI agents that accelerate impact