Skills
Required
data
software
empathy
technology
engineering
agility
strategy
design
architecture
management
consistency
api
resolve
etl
test
sql
python
jenkins
azure
devops
integrity
logic
reconciliation
checks
drive
metrics
reporting
triage
analysis
testing
database
informatica
automation
scripting
deduplication
orchestration
airflow
spark
hive
Job
Description
Role: Data QA Lead Experience Required- 8+ Years Location- India/Remote Company Overview At Codvo.ai, software and people transformations go hand-in-hand. We are a global empathy-led technology services company. Product innovation and mature software engineering are part of our core DNA. Respect, Fairness, Growth, Agility, and Inclusiveness are the core values that we aspire to live by each day. We continue to expand our digital strategy, design, architecture, and product management capabilities to offer expertise, outside-the-box thinking, and measurable results. The Data Quality Analyst is responsible for ensuring the quality, accuracy, and consistency of data within the Customer and Loan Master Data API solution. This role will work closely with data owners, data modelers, and developers to identify and resolve data quality issues. Key Responsibilities Lead and manage end-to-end ETL/data validation activities. Design test strategy, plans, and scenarios for source-to-target validation. Build automated data validation frameworks (SQL/Python/Great Expectations). Integrate tests with CI/CD pipelines (Jenkins, Azure DevOps). Perform data integrity, transformation logic, and reconciliation checks. Collaborate with Data Engineering, Product, and DevOps teams. Drive test metrics reporting, defect triage, and root cause analysis. Mentor QA team members and ensure process adherence. Must-Have Skills 8+ years in QA with 4+ years in ETL testing. Strong SQL and database testing experience. Proficiency with ETL tools (Airbyte, DBT, Informatica, etc.). Automation using Python or similar scripting language. Solid understanding of data warehousing, SCD, deduplication. Experience with large datasets and structured/unstructured formats. Preferred Skills Knowledge of data orchestration tools (Prefect, Airflow). Familiarity with data quality/observability tools. Experience with big data systems (Spark, Hive). Hands-on with test data generation (Faker, Mockaroo). Show more Show less
