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Role OverviewWe’re looking for a hands-on Data Engineer to own the data layer of customer go-lives — ensuring migrations are validated, analytics pipelines are hardened, and business dashboards are powered by accurate, performant data. You’ll be responsible for validating and signing off on end-to-end data migrations, building high-quality SQL models, and implementing automated data quality checks to catch issues early.
This is a highl technical and impact-driven role focused on migration testing, SQL performance tuning, and data quality automation — aligning with AWS and industry best practices.
Key Responsibilities- End-to-End Migration Validation: Design and execute functional and performance validation for data migrations — including parity, nullability, PK/FK, duplication, and sampling checks — with complete documentation and sign-off aligned to AWS migration testing guidelines.
- Advanced SQL Development: Write and optimize analytical SQL (CTEs, window functions, incremental loads). Use EXPLAIN plans to tune query performance and ensure indexes and statistics support BI workloads.
- Automated Data Quality Frameworks: Implement and maintain data validation frameworks using Great Expectations, Deequ, or similar tools. Automate validation and publish Data Docs to ensure transparency across teams.
- Modeling & Documentation (dbt): If using dbt, build models with tests, exposures, and documentation to ensure traceability between dashboards and upstream data sources.
- Orchestration & Reliability: Productionize data validation and transformation jobs within Airflow DAGs, ensuring welldefined SLAs, alerts, and reliable pipeline operations.
- (Optional) Cloud Data Engineering: Build incremental pipelines and optimize batch processing for Snowflake (Streams & Tasks) or PostgreSQL, ensuring performance and cost efficiency.
- Experience: 4–7+ years as a Data Engineer or Analytics Engineer.
- SQL Expertise: Advanced proficiency in SQL and strong RDBMS fundamentals (PostgreSQL required), with proven experience in query tuning using EXPLAIN/analyze.
- Migration Validation: Hands-on experience designing and executing data migration validation (parity, integrity, and performance testing).
- Tooling Knowledge: Experience with one or more of the following — dbt, Great Expectations or Deequ/PyDeequ, Airflow.
- Version Control: Comfortable with Git-based workflows and CI/CD integration.
- Experience with Snowflake (Streams, Tasks, cost optimization, and warehouse tuning).
- Exposure to BI tools such as Looker, Power BI, Tableau, or Metabase.
- Working knowledge of Python for lightweight data transformations and validation frameworks.
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