Manager, Quality Engineering - AI Automation & Data

Job highlights

• Automate testing of product data flows,APIs,services,and batch,incremental,CDC,streaming,and near-real-time pipelines across ingestion,transformation,storage,semantic,reporting,and consumption layers. . Validate source-to-target mappings,transformations,business rules,calculations,aggregations,data contracts,derived attributes,and API responses.
• Preferred: .

Job Summary

Join Us as Manager, Quality Engineering - AI Automation and Data!

Are you a hands-on quality engineering leader passionate about building high-performing teams and advancing automation through AI-assisted testingJoin ORBCOMM and play a critical role in ensuring the quality, reliability, and accuracy of connected-device and operational data across our products, platforms, and customer-facing applications.

This role will work onsite at Hyderabad, India.

What You’ll Do

Lead and Develop a High-Performing Quality Engineering Team

• Lead, mentor, and grow a team of Quality Engineers and SDETs through hiring, onboarding, coaching, performance management, and career development.
• Drive consistent quality engineering practices across multiple delivery teams.
• Foster a culture of technical excellence, accountability, continuous improvement, and innovation.

Build AI-Assisted, Automation-First Quality Engineering Capabilities

• Build and govern reusable automation frameworks using Python, SQL, PyTest, and fit-for-purpose data quality tools.
• Leverage AI-assisted testing approaches to improve test coverage, efficiency, and scalability.
• Establish governance and best practices for intelligent automation and AI-assisted testing workflows.

Validate End-to-End Data, Platform, and Application Quality

• Automate testing of product data flows, APIs, services, and batch, incremental, CDC, streaming, and near-real-time pipelines across ingestion, transformation, storage, semantic, reporting, and consumption layers.
• Validate source-to-target mappings, transformations, business rules, calculations, aggregations, data contracts, derived attributes, and API responses.
• Reconcile customer-facing KPIs and business outcomes across data platforms, APIs, dashboards, reports, exports, alerts, and downstream applications.
• Verify consistency between batch and live processing, including scenarios involving provisional or late-arriving data.

Implement Data Quality Controls and Release Quality Gates

• Implement automated controls for data accuracy, completeness, consistency, validity, uniqueness, referential integrity, schema drift, freshness, lineage, tenant isolation, and authorized data usage.
• Monitor for anomalous data volumes, distributions, and quality trends throughout ingestion, processing, and consumption layers.
• Integrate automated testing and regression suites into Azure DevOps, GitHub Actions, or equivalent CI/CD pipelines.
• Establish automated quality gates that support reliable and secure software releases.

Who You Are

You are a technical quality engineering leader who combines strong people leadership with deep expertise in automation, data validation, and modern quality engineering practices. You excel at solving complex problems, building scalable frameworks, and improving confidence in data-driven products.

If you have:

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Information Technology, or a related field, or equivalent practical experience.
• 8+ years of experience in software quality engineering, test automation, SDET, data quality, or related technical roles.
• 3+ years of experience leading quality engineering, automation, or data quality teams.
• Demonstrated success hiring, coaching, and developing high-performing technical teams.
• Strong hands-on expertise with Python-based test automation, advanced SQL, reusable framework design, AI-assisted engineering workflows, source control, code review, and CI/CD integration.
• Deep understanding of ETL/ELT processes, data lakes, lakehouses, data warehouses, dimensional models, streaming or event-driven data, semantic layers, and large-scale data validation.
• Experience testing cloud data platforms such as Microsoft Fabric and Azure data services, or comparable platforms within AWS or Google Cloud.
• Strong API and integration testing experience, including REST APIs, JSON/XML schemas, authentication, contract validation, retry mechanisms, rate limiting, integrations, and API-to-data reconciliatio…