Azure Data Engineer (Standard)
Tenarai (formerly Infogain) · IT Services & Consulting
- Noida / Pune / Bangalore / Mumbai / Hyderabad / Chennai / Gurugram / Kochi, India
- On-site
- Posted today
- Data & AI
- Full-Time / Contract
About the job
<p><strong>Core Skills</strong></p><h3><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Role Summary</span></span></h3><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">We are looking for someone with a strong testing/QA orientation to validate data pipelines, transformations, and reporting layers across our data platform. This role sits at the intersection of data engineering and quality assurance — the person will not just execute test scripts, but understand the underlying pipeline architecture (source-to-target mappings, transformation logic, and business rules) well enough to design meaningful test cases, catch data-quality issues before they reach production, and reconcile numbers across source and target systems.</span></span></p><h3><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Key Responsibilities</span></span></h3><ul><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Design, build, and execute test plans for ETL/ELT pipelines, covering functional testing, data validation, regression testing, and reconciliation between source and target systems (e.g., source system ? bronze ? silver ? gold layers)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Validate transformation logic (mappings, calculations, aggregations, routines) against business requirements and source system behavior</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Perform row-count, checksum, and value-level reconciliation between legacy and migrated/modernized data platforms</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Write and maintain SQL-based test scripts to independently verify pipeline outputs (not just rely on developer-provided validation)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Identify, document, and track data-quality defects; work with data engineers to root-cause and resolve discrepancies</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Build and maintain reusable test data sets, test harnesses, and automated validation scripts where feasible</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Test incremental/delta load logic in addition to full loads, including edge cases (late-arriving data, nulls, duplicates, boundary conditions across fiscal periods)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Participate in UAT/SIT cycles, coordinate with business stakeholders to validate reports/dashboards against underlying data</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Maintain clear test documentation: test cases, test evidence, defect logs, and sign-off criteria</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Support release management processes — ensure changes are tested and validated before promotion to production</span></span></p></li></ul><h3><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Required Technical Skills</span></span></h3><ul><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Strong SQL skills — able to write complex queries independently for validation and reconciliation (joins, aggregations, window functions)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Understanding of ETL/ELT concepts and data pipeline architecture (source, staging, transformation, target layers)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Experience testing data on at least one modern data platform (e.g., Databricks, Microsoft Fabric, Snowflake, SAP BW/4HANA, Synapse)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Familiarity with data modeling concepts (star schema, fact/dimension tables, slowly changing dimensions)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Exposure to Python or PySpark for writing automated data validation scripts is a plus</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Experience with test management/defect tracking tools (e.g., JIRA, Azure DevOps, HP ALM)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Understanding of data quality dimensions: completeness, accuracy, consistency, timeliness</span></span></p></li></ul><h3><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Preferred / Good to Have</span></span></h3><ul><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Experience testing SAP-sourced data (BW, S/4HANA, ECC) migrating to a cloud data platform</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Familiarity with BI/reporting tools (Power BI, SAP Analytics Cloud, Tableau) to validate report-level numbers against underlying data</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Exposure to CI/CD pipelines and test automation frameworks for data (e.g., Great Expectations, dbt tests)</span></span></p></li><li><p><span style="color:rgb(0, 0, 0);"><span style="background-color:transparent;">Basic understanding of finance, supply chain, or procurement domain data (GL, P&amp;L, cost center, asset accounting, etc.)</span></span></p></li></ul>