20 Namely Data Engineer Jobs Hiring Near You

Principal Data Engineer - QA

We’re looking for a Principal Data Engineer - QA in Pune or Hyderabad who believes data quality isn’t a phase — it’s a discipline. You’ll join a growing team with outsized ownership, building the quality engineering practice for a modern enterprise data platform running on Snowflake, dbt, Matillion, and Salesforce Agentforce. You’ll have direct executive visibility and the mandate to define what “trusted data” means across the company. This role is deeply hands‑on. You’ll spend most of your time writing code — SQL tests, Python automation, validation frameworks — not just reviewing test plans. But you’ll also set the strategic direction: what gets tested, how, and why. We need someone who can hit the ground running from day one — you’ve built quality practices before, you know what works and what doesn’t, and you’ll start contributing immediately. We also want someone who brings ideas: you see a gap in coverage, you don’t just flag it — you propose a solution and build it. If you’re the kind of engineer who gets genuinely frustrated by a dashboard showing the wrong number and won’t rest until you’ve traced it back to the root cause, this is your job.

Responsibilities

• Own and define the end‑to‑end QA and test strategy for enterprise data and analytics platforms while remaining deeply hands‑on in execution.
• Design, build, and maintain scalable automated testing frameworks, personally contributing code for data validation, regression, reconciliation, and pipeline monitoring.
• Lead test automation using SQL and Python to validate Snowflake data models, dbt transformations, and downstream analytics.
• Apply AI‑driven testing techniques including test generation, anomaly detection, predictive regression analysis, and intelligent test selection to improve coverage.
• Partner with Data Engineering to embed quality directly into dbt models, ELT pipelines, and orchestration workflows.
• Validate analytics and reporting across Tableau, Salesforce, and downstream consumer tools, ensuring metric consistency and business accuracy.
• Define and enforce data quality standards, SLAs, and KPIs across all business functions.
• Perform root cause analysis on complex data issues with a high sense of urgency and own the fix through to resolution.
• Mentor and up‑level QA and data engineers through test design reviews and automation code reviews.
• Communicate quality risks, coverage gaps, and readiness metrics to business and IT leadership.

Qualifications

• 7+ years in Quality Engineering with a strong focus on data, analytics, or data warehouse testing.
• Built quality practices before and can orient yourself in an unfamiliar data platform quickly, delivering value in weeks not months.
• Deep experience testing Snowflake-based data platforms or equivalent modern cloud data warehouses.
• Strong experience with dbt and testing data transformations including models, snapshots, seeds, and incremental logic.
• Write the automation code yourself: Python frameworks, SQL test suites, monitoring scripts.
• Understand dimensional modeling, ELT architecture, conformed dimensions, grain, and idempotency at a foundational level.
• Identify what’s broken, what’s missing, and what to do about it without waiting for direction.
• Bring solutions, not just bug reports.
• Notice drift, edge cases, and silent failures that others miss.
• Stay current with new testing approaches, platform features, and quality engineering practices because you genuinely want to get better.
• Bachelor’s degree in Computer Science, Engineering, or related field.

Extra dose of awesome if you have…

• Experience testing data pipelines powered by Fivetran, Apache Airflow, Matillion, or similar orchestration tools.
• Experience implementing or experimenting with AI/ML-assisted testing tools.
• Experience with data observability or data quality frameworks such as Great Expectations, Monte Carlo, or Snowflake DMFs.
• SnowPro Core or SnowPro Advanced Data Engineer certification.
• Background validating analytics for Sales, Marketing, Finance, or HR domains.
• Exposure to advanced analytics, data science workflows, or AI agent validation.
• Familiarity with Salesforce data structures and their downstream analytics impact.
• AWS cloud experience.

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