Manager, Quality Engineering - AI Automation & Data

Orbcomm Technologies; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Orbcomm Technologies is seeking a hands-on Manager, Quality Engineering to lead AI automation initiatives and data quality across products, platforms, and customer-facing applications. This on-site role in Hyderabad drives testing strategy and builds high-performing teams.

You will mentor Quality Engineers and SDETs, establish reusable automation frameworks with Python, SQL, and AI-assisted testing, and govern data quality controls throughout end-to-end pipelines.

Qualifications

• 8+ years in software quality engineering, test automation, SDET, data quality, or related roles.
• 3+ years leading quality engineering, automation, or data quality teams.
• Demonstrated success hiring, coaching, and developing high-performing technical teams.

Responsibilities

• 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.

Skills

Python automation

Advanced SQL

AI-assisted testing

CI/CD integration

Test framework design

Data validation

API testing

Education

Bachelor's degree in CS/Software Eng

Tools

Microsoft Fabric

Azure Data Factory

Azure Data Lake

Synapse

Databricks

Delta Lake

Spark

PySpark

Azure DevOps

GitHub Actions

Power BI

Job description

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 reconciliation.

Preferred:

• Experience with Microsoft Fabric, Azure Data Factory, Azure Data Lake, Synapse, Databricks, Delta Lake, Spark, or PySpark.

• Experience applying generative AI, machine learning, or intelligent automation to testing, data profiling, anomaly detection, log analysis, or defect triage.
• Experience validating telemetry, IoT, telematics, logistics, transportation, connected-device, or other high-volume operational data ecosystems.
• Experience with Power BI semantic models, KPI reconciliation, data governance, data catalogs, lineage, and business reporting controls.
• Experience establishing governance for AI-assisted testing and intelligent automation, including traceability, human review processes, and management of generated tests and recommendations.