Cittabase is Hiring hiring a Sr. QE Engineer for a large-scale Data & Analytics program spanning multiple source applications, a cloud data warehouse, and BI reporting tools. This role requires strong Data Warehouse and Data Engineering fundamentals as the primary bar, write and optimize SQL against large datasets, and build validation/automation logic in Java and/or Python.Core ResponsibilitiesOwn quality engineering for assigned areas of the data platform — covering data validation, transformation logic checks, BI-layer testing, and pipeline health across multiple source applications, the cloud data warehouse, and BI reporting toolsWrite and review complex SQL against large datasets — joins, set operations, aggregations, and layer-aware comparisons appropriate to each stage of the DWBuild Java and/or Python utilities/scripts to support test automation, data validation, and pipeline checks — including designing reusable, maintainable logic and handling large datasets efficientlyUnderstand and account for aggregation-grain differences across DW layersExtract and validate data from source applications without direct database access, using API-based or scripted extraction approachesWork with data transformation pipelines to understand transformation logic and trace data lineage across layersSupport performance validation for data pipelines and BI dashboard loadCollaborate with the automation team to integrate validation logic into the existing CI/CD pipeline — deep BDD/Cucumber authorship not required,



but willingness to learn and work within an existing framework is expectedContribute to test case design, defect triage, and quality reporting for the assigned scopeRequired Skills & ExperienceData Warehouse / Data Engineering:5+ years hands-on experience with a cloud data warehouse or lakehouse platformStrong SQL — able to write non-trivial queries from scratch joins across large tables, set-difference logic, aggregation and grain reasoningSolid understanding of DW layering concepts (raw/staging/curated or bronze/silver/gold) and why data shape/volume legitimately changes across layersExperience validating ETL/ELT pipelines end-to-end, not just isolated target-side checksImmediate Joiners PreferredFamiliarity with data transformation pipelines (reading/tracing transformation logic; authoring is a plus)Programming (required):Robust Java or Python fundamentals (both is a plus) — must be able to design clean, reusable code for data validation and automation tasks, not just scriptsComfortable writing utilities for data extraction and transformation (e.G., calling REST APIs) where direct DB access isn't availablePlatform/Tooling exposure Exposure to CRM or ERP source application data structuresExposure to BI reporting tools (report structure, security/row-level filtering, data refresh mechanics)BDD frameworks (Cucumber-JVM, Gherkin)Test management tools (e.G., Zephyr/JIRA)CI/CD tooling (e.G., GitHub Actions)What Disqualifies a CandidateWeak on fundamental SQL (joins, aggregation, set operations)Cannot write coherent, structured Java/Python code beyond basic scriptingNo hands-on data warehouse or ETL/ELT validation experience

📌 Qa/Qe Engineer (Chennai)
🏢 Cittabase Solutions
📍 Chennai