Data Engineer - Analytics & Migration

<p><p><b>Location:</b> Work from home<br/><br/><b>Role Summary:</b><br/><br/>Were 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.

Youll 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 highly technical and impact-driven role focused on migration testing, SQL performance tuning, and data quality automation aligning with AWS and industry best practices.<br/><br/><b>Key Migration Validation :</b> 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.<br/><br/><b>Advanced SQL Development :</b> 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.<br/><br/><b>Automated Data Quality Frameworks :</b> Implement and maintain data validation frameworks using Great Expectations, Deequ, or similar tools.

Automate validation and publish Data Docs to ensure transparency across teams.<br/><br/><b>Modeling & Documentation (dbt) :</b> If using dbt, build models with tests, exposures, and documentation to ensure traceability between dashboards and upstream data sources.<br/><br/><b>Orchestration & Reliability :</b> Productionize data validation and transformation jobs within Airflow DAGs, ensuring welldefined SLAs, alerts, and reliable pipeline operations.<br/><br/><b>(Optional) Cloud Data Engineering :</b> Build incremental pipelines and optimize batch processing for Snowflake (Streams & Tasks) or PostgreSQL, ensuring performance and cost efficiency.<br/><br/><b>Minimum :</b> 4-7+ years as a Data Engineer or Analytics Engineer.<br/><br/><b>SQL Expertise :</b> Advanced proficiency in SQL and strong RDBMS fundamentals (PostgreSQL required), with proven experience in query tuning using EXPLAIN/analyze.<br/><br/><b>Migration Validation :</b> Hands-on experience designing and executing data migration validation (parity, integrity, and performance testing).<br/><br/><b>Tooling Knowledge :</b> Experience with one or more of the following dbt, Great Expectations or Deequ/PyDeequ, Airflow.<br/><br/><b>Version Control :</b> Comfortable with Git-based workflows and CI/CD integration.<br/><br/><b>Nice to Have :</b><br/><br/>- Experience with Snowflake (Streams, Tasks, cost optimization, and warehouse tuning).<br/><br/>- Exposure to BI tools such as Looker, Power BI, Tableau, or Metabase.<br/><br/>- Working knowledge of Python for lightweight data transformations and validation frameworks.</p><br/></p> (ref:hirist.tech)