Senior Manager, AI & Data Quality Engineering

Overview

Lead distributed Data Quality, Reporting QA, Application QA and automation teams across India and the USA, owning the Insights-wide quality operating model and driving AI-assisted quality strategy.

What you'll do

• Lead distributed Data Quality, Reporting QA, Application QA, and automation teams across India and the USA.
• Define and own the Insights-wide quality operating model, standards, governance, and release gates.
• Establish automated validation for data completeness, accuracy, freshness, reconciliation, referential integrity, duplication, and anomaly detection.
• Lead quality strategy for Financial Data Mart and CMDM modernization, including Databricks, Synapse, Power BI, row-level security, workspace synchronization, and customer rollouts.
• Govern testing across the full release lifecycle: data pipelines, semantic models, reports, application workflows, APIs, embeds, scheduling, and production monitoring.
• Expand automated test frameworks using Playwright, Cucumber, TypeScript, Python, SQL, and CI/CD pipelines.
• Partner with Data Engineering, BI, Application Engineering, Product, Support, and SRE teams to resolve systemic quality issues.
• Use production-quality signals such as report load time, refresh failures, data incidents, defect escape rate, and customer-impact trends to prioritize improvements.
• Build talent across India and the USA through hiring, coaching, mentoring, succession planning, and consistent performance management.
• Create effective follow-the-sun operating practices with clear ownership, handoffs, escalation paths, and shared documentation.
• Drive AI-assisted test-case generation, test maintenance, and regression prioritization.
• Build intelligent defect triage, routing, root-cause investigation, and ticket-drafting workflows.
• Apply anomaly detection and AI-assisted reconciliation to data pipelines and reporting outputs.
• Use AI agents and natural-language workflows to improve quality operations while preserving human approval for consequential decisions.
• Define evaluation standards for AI outputs, including accuracy, consistency, explainability, hallucination risk, business-rule compliance, and drift.
• Establish guardrails for privacy, security, auditability, model governance, and safe use of enterprise data.

What you'll need

• Significant experience leading QA, Data Quality, Data Engineering QA, BI QA, or Quality Engineering teams.
• Experience managing distributed teams across multiple geographies and time zones.
• Strong SQL expertise for data validation, reconciliation, troubleshooting, and test-data preparation.
• Hands-on experience with Databricks and modern data-platform testing.
• Experience with Azure Data Factory, Synapse, or comparable data-pipeline platforms.
• Strong Power BI experience, including paginated reports, semantic models, row-level security, workspaces, refreshes, exports, and report validation.
• Demonstrated experience designing quality automation strategies across data, reporting, APIs, and applications.
• Strong AI, Generative AI, or machine-learning experience applied to software quality, data quality, analytics, or engineering productivity.
• Experience evaluating AI outputs for accuracy, consistency, bias, hallucination risk, and business-rule compliance.
• Knowledge of AI agents, LLM workflows, prompt engineering, model evaluation, and human-in-the-loop controls.
• Experience with Playwright, Selenium, Cypress, Python, TypeScript, or equivalent automation technologies.
• Experience with GitHub, Azure DevOps, CI/CD, Jira, and release-management practices.
• Strong communication skills and the ability to influence senior technical and business stakeholders.

Nice to have

• Experience building configuration-driven Data Quality frameworks using ADF, SQL, Databricks, or Synapse.
• Experience with Power BI APIs, automated report comparison, and large-scale regression testing.
• Experience with OpenTelemetry, Honeycomb, Application Insights, or comparable observability platforms.
• Experience applying AI to ticket triage, incident investigation, test maintenance, or defect prioritization.
• Experience with data governance, metric standardization, master data, or customer-facing analytics platforms.
• Experience supporting high-availability reporting platforms and production incident management.

Details

• Hybrid role based at the Hyderabad office, India, working from the office 2-3 days a week.
• Works across India and USA time zones (follow-the-sun operating model).