QA Lead – Data Engineering, ETL & Automation Testing

Description
Everlytics is looking for an experienced QA Lead to spearhead quality assurance across complex end-to-end data pipelines, modern data engineering workflows, and large-scale analytical platforms. We are looking for someone with deep expertise in testing orchestrated data flows, data lakehouse architectures, and mission-critical insurance/BFSI systems.
Technologies
Manual & Automation Testing, Python (pytest/unittest), SQL, Apache Airflow (DAGs & Pipeline Orchestration), API Testing, ETL/ELT & Data Lakehouse Testing, Great Expectations / Soda Core (Data Quality), OpenMetadata (Governance & Lineage), Apache JMeter / Locust (Performance), CI/CD Tools (GitHub Actions, Jenkins), Test Management & Defect Tracking (Jira, Kiwi TCMS, Confluence), and Agile/Scrum methodologies.
Experience Required
5+ years of experience in software testing and quality assurance leadership, focusing on test planning, execution, defect management, and coordinating end-to-end testing for data platforms.
Specialized BFSI & Insurance Domain Expertise: Proven experience testing insurance core platforms, policy administration systems, claims processing pipelines, risk scoring models, and financial/actuarial data flows. Strong understanding of insurance industry regulations, data accuracy, auditability, and compliance standards.
E2E Data Pipeline & Lakehouse Testing: Strong experience testing modern data architectures, covering source ingestion, transformations, and consumption layers.
Hands-on Technical Validation: Expertise in SQL-based data validation, source-to-target reconciliation, pipeline dependency tracing, and identifying anomalies across automated workflows.
Proven QA Governance: Experience defining test strategies, test scenarios, data acceptance criteria, entry/exit criteria, and release readiness gates.
Cross-Functional Collaboration: Track record of managing defect lifecycles, test metrics, execution reports, stakeholder communication, and business sign-offs in Agile environments.
Technical & Functional Skills
Test Strategy & Management: Test planning, effort estimation, test coverage mapping, risk assessment, defect lifecycle management, release readiness governance, and CI/CD quality gate enforcement using Jira, or Kiwi TCMS.
E2E Pipeline & Orchestration Testing: Validating orchestrated data workflows (Apache Airflow DAG structures, dependencies, retries, timeouts, and idempotency) and automated testing using Python (pytest, unittest).
Insurance & Financial Data Quality Frameworks: Using data quality and assertion tools such as Great Expectations or Soda Core to validate nulls, duplicates, schema evolution, freshness, and custom insurance business-rule checks (e.g., premium calculations, claim settlements).
Data Governance, Lineage & Reconciliation: Verifying metadata ingestion, catalog completeness, column-level lineage tracking via OpenMetadata, alongside robust SQL-based source-to-target record and aggregate reconciliation.
Performance, Security & Access Validation: Load and concurrency testing using Apache JMeter or Locust to handle high-volume transaction spikes; role-based access control (RBAC) and data masking/tokenization to secure PII and sensitive policyholder data.
Roles & Responsibilities
Define and Execute E2E Test Strategies: Formulate comprehensive test strategies, test plans, scope, and acceptance criteria specifically tailored for data pipelines, insurance platforms, and analytics rollouts.
Test Orchestrated Pipelines & Workflows: Validate complex Apache Airflow DAGs and automated Python scripts, ensuring robust dependency management, failure handling, retry logic, and seamless data movement across the ecosystem.
Oversee Insurance & BFSI Data Quality: Ensure rigorous validation of policy, claims, billing, and risk datasets, ensuring zero data loss, exact source-to-target financial reconciliations, and regulatory compliance.
Drive Data Governance & Lineage Verification: Collaborate with engineering teams to verify that technical/business metadata, data lineage graphs, and tagging rules are accurately populated and tracked via OpenMetadata.
Lead SIT, UAT & End-to-End Testing: Drive system integration testing (SIT) and user acceptance testing (UAT) execution, coordinating test data preparation, environment availability, and business stakeholder reviews with insurance domain experts.
Monitor Performance & Resilience: Evaluate ingestion throughput, transformation duration, API response times, and concurrent query performance using Apache JMeter or Locust to ensure SLAs are met under peak loads.
Track Metrics & Report Release Readiness: Monitor test execution coverage, defect trends, and quality metrics; prepare test summary reports and clear go/no-go recommendations for production deployments.
Drive Continuous Quality Improvement: Champion best practices in test automation, continuous testing within CI/CD pipelines, and rigorous data quality governance across cross-functional engineering and business teams.
Generic Skills
Strong communication skills
Strong analytical skills
Strong team management, and decision-making skills
Benefits
Health Insurance
Mentoring (in a pure-play Data Science company)

Work arrangement
Yes