Lead Quality Engineer

Company Description Cittabase Solutions is a specialized Data & Analytics company delivering enterprise-grade products and services in Data Management, Business Intelligence, AI, and Analytics.
We are hiring a Lead 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 Responsibilities
Own 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 tools
Write and review complex SQL against large datasets — joins, set operations, aggregations, and layer-aware comparisons appropriate to each stage of the DW
Build Java and/or Python utilities/scripts to support test automation, data validation, and pipeline checks — including designing reusable, maintainable logic and handling large datasets efficiently
Understand and account for aggregation-grain differences across DW layers
Extract and validate data from source applications without direct database access, using API-based or scripted extraction approaches
Work with data transformation pipelines to understand transformation logic and trace data lineage across layers
Support performance validation for data pipelines and BI dashboard load
Collaborate 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 expected
Contribute to test case design, defect triage, and quality reporting for the assigned scope
Required Skills & Experience
Data Warehouse / Data Engineering (primary bar):
6+ years hands-on experience with a cloud data warehouse or lakehouse platform
Strong SQL — able to write non-trivial queries from scratch: joins across large tables, set-difference logic, aggregation and grain reasoning
Solid understanding of DW layering concepts (raw/staging/curated or bronze/silver/gold) and why data shape/volume legitimately changes across layers
Experience validating ETL/ELT pipelines end-to-end, not just isolated target-side checks
Familiarity with data transformation pipelines (reading/tracing transformation logic; authoring is a plus)
Programming (required):
Strong Java or Python fundamentals (both is a plus) — must be able to design clean, reusable code for data validation and automation tasks, not just scripts
Comfortable writing utilities for data extraction and transformation (e.g., calling REST APIs) where direct DB access isn't available
Platform/Tooling exposure (nice to have, trainable):
Exposure to CRM or ERP source application data structures
Exposure 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 Candidate
Weak on fundamental SQL (joins, aggregation, set operations)
Cannot write coherent, structured Java/Python code beyond basic scripting
No hands-on data warehouse or ETL/ELT validation experience

Work arrangement
No