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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading quality engineering teams, implementing automation frameworks, and ensuring data quality across various platforms. Proficient in Python, SQL, and AI-assisted testing methodologies to enhance software quality and operational efficiency.
Highest-signal resume keywords
Python-Based Test AutomationAdvanced SQLCI/CD IntegrationData Quality GovernanceAPI Testing
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Test AutomationData ValidationETL/ELT ProcessesReusable Framework DesignAI-Assisted Engineering Workflows
Soft Skills
Team LeadershipCoachingPerformance ManagementContinuous ImprovementTechnical Excellence
Tools & Technologies
Azure DevOpsGitHub ActionsMicrosoft FabricAzure Data FactoryPower BI
Industry Keywords
Data QualityCloud Data PlatformsStreaming DataData GovernanceBusiness Reporting Controls
Tech Stack
Tools & technologiesAWSAzureCloudETLIoTPySparkPythonSparkSQL
About the role
Key responsibilities & impact- 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 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
- Automate testing of product data flows, APIs, services, and batch, incremental, CDC, streaming, and near-real-time pipelines
- Validate data 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 provisional or late-arriving data
- Implement automated controls for data accuracy, completeness, consistency, validity, uniqueness, referential integrity, schema drift, freshness, lineage, tenant isolation, and authorized data usage
- Monitor 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
Requirements
What you’ll need- 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
- Preferred: Experience applying generative AI, machine learning, or intelligent automation to testing, data profiling, anomaly detection, log analysis, or defect triage
- Preferred: Experience validating telemetry, IoT, telematics, logistics, transportation, connected-device, or other high-volume operational data ecosystems
- Preferred: Experience with Power BI semantic models, KPI reconciliation, data governance, data catalogs, lineage, and business reporting controls
- Preferred: Experience establishing governance for AI-assisted testing and intelligent automation, including traceability, human review processes, and management of generated tests and recommendations
- Leverage AI-enabled tools and automation to support daily tasks and productivity
- Commitment to continuous learning in digital and AI literacy
Benefits
Comp & perks- Health and wellness coverage
- Paid time off
- Retirement or savings programs
- Flexible work arrangements
- Insurance coverage
- Lifestyle perks
- Employee discounts
- Accessibility accommodations throughout all aspects of the selection process