Data Engineer

About this role

Batch: 2022/2023/2024. About the role: - Build and maintain production data systems that power analytics and machine learning workflows - Design and implement scalable data pipelines using modern cloud-native tools - Collaborate with cross-functional teams to translate business requirements into robust data architecture - Own the full lifecycle of data transformations from ingestion through serving What you'll work on: - Writing and optimizing complex SQL queries including joins, aggregations and window functions, with the ability to diagnose and resolve performance bottlenecks - Building production-grade PySpark and Spark SQL applications on Databricks - Designing dimensional data models including star schemas, fact and dimension tables, SCD Type 2 implementations and incremental load patterns - Creating reliable dbt transformation pipelines with models, tests, sources and incremental strategies - Leveraging AWS services including Lambda, S3, CloudFront and SQS to build resilient infrastructure - Using AI-native development tools like Claude Code and Cursor as core parts of your workflow, not peripheral add-ons What we're looking for: - 2-3 years of hands-on data engineering work with deployed production systems, not prototypes or notebook experiments - Expert-level SQL skills with deep understanding of query optimization and performance tuning - Proven experience with Databricks as your preferred platform for PySpark and Spark SQL work - Solid grasp of dimensional modeling concepts and the ability to apply them confidently in complex scenarios - Track record shipping incremental loads and data models that can be re-run reliably without data corruption - Hands-on AWS experience across Lambda, S3, CloudFront, SQS and related services - Problem-solving mindset that navigates the tradeoff between correctness and velocity, with clear communication around decisions - Self-directed approach to learning, adaptability in fast-moving environments and awareness of emerging trends in data engineering Good to know: - Foundational knowledge in ML and AI fundamentals is a bonus