Data Analytics Lead Engineer

Quick Facts

Data Analytics; Technology; Full time

Description

Hands-on data engineering role to design, build, and operate scalable data pipelines and cloud-based data architectures within the Lending business (Mortgage and Personal Loans). You will build and maintain production-grade data systems that power lending analytics at scale, and apply an understanding of AI/ML integration on top of a strong data engineering foundation.

Responsibilities

• Build, deploy, and manage end-to-end data pipelines that ingest, transform, and deliver large-scale lending datasets with high reliability and performance.
• Design and implement scalable data architectures on cloud platforms, selecting tools and approaches across data lakes, data warehouses, and streaming environments.
• Architect and implement data schemas (relational, dimensional, normalized, or partitioned models) to meet performance, scalability, and business requirements.
• Write and optimize complex SQL queries against large-scale datasets, improving pipeline efficiency with sound distributed/parallel processing decisions.
• Monitor, diagnose, and resolve operational and data quality issues across pipelines to ensure accuracy, completeness, and timely delivery.
• Apply generative AI tools to accelerate core engineering tasks such as code generation, query optimization, and data summarization where appropriate.
• Contribute to data engineering standards and collaborate with Business Analysts, Data Engineers, and Data Governance teams to translate business requirements into robust technical solutions.

Requirements

• 6+ years of hands-on experience building and managing data pipelines, data warehouses, and data lake solutions using Hadoop, Apache Spark, PySpark, Databricks, Delta Lake, Hive, Impala, and Iceberg.
• Practical experience with cloud data platforms such as Snowflake or Cloudera, building and automating ETL and data ingestion workflows.
• Fluency in Python, Scala, or Shell scripting used actively for data engineering, pipeline development, and automation.
• Strong ability to design and query relational and non-relational data stores; understanding of schema design trade-offs and data modelling principles.
• Hands-on experience with workflow scheduling tools such as Autosys or Apache Airflow.
• Confident use of DevOps practices: version control, build tools, unit testing, monitoring, and change management.
• Experience with data visualization platforms such as Tableau or Cognos.
• Bachelor’s degree (Master’s preferred).

Benefits

• Hybrid working model: 3 days in the office and 2 days working remotely.
• Access to large-scale, complex data environments and modern cloud infrastructure with direct business impact.
• Continuous learning and technical development support, including backing for relevant cloud and platform certifications.
• Collaborative team environment with hands-on technical contribution.
• Competitive compensation and comprehensive benefits package (medical, dental & vision, 401(k), life/accident/disability, wellness programs, paid time off, paid holidays).