Data Engineer III - Data & AI Enablement

The Data Engineer III designs, constructs, tests, and maintains highly scalable data management systems. Data Engineer III is responsible for developing complex data pipelines and ensuring data quality, which involves transforming raw data into a form that can be easily analyzed.

What makes you an ideal candidate:

• Design and build scalable data pipelines and workflows on Azure Databricks, leveraging Delta Lake, Unity Catalog, and Medallion (Bronze/Silver/Gold) Lakehouse architecture.
• Lead end-to-end migration of legacy SAS analytical workloads to Databricks/PySpark, including SAS macro translation, data validation, and output reconciliation.
• Drive migration of Oracle databases and stored procedures to Azure-native services (Azure SQL, Synapse Analytics, or Delta Lake), ensuring data fidelity and business continuity.
• Architect and implement ELT/ETL frameworks supporting batch and near-real-time data ingestion from diverse financial source systems.
• Design dimensional models, data vault patterns, and lakehouse table structures optimized for financial analytical and regulatory reporting workloads.
• Build and maintain data pipelines that power AI/ML models, including feature engineering, training data preparation, and inference feeds using Databricks MLflow and Feature Store.
• Implement LLM and RAG (Retrieval-Augmented Generation) pipeline patterns for internal analytics and tooling where applicable.
• Design and consume REST APIs for data ingestion, orchestration, and data product exposure; build integrations with third-party financial data providers.
• Implement event-driven and streaming data patterns using Azure Event Hubs, Service Bus, and Kafka.
• Enforce data governance policies in Unity Catalog: column-level security, row-level filtering, PII masking, and audit logging.
• Implement data quality frameworks with automated alerting and lineage tracking.
• Ensure all data solutions comply with applicable financial regulations: SOX, BCBS 239, GDPR/CCPA, CCAR, DFAST, Basel III/IV.
• Apply data security controls including encryption, Azure Key Vault management, Private Endpoints, VNet integration, and Managed Identity.
• Define engineering standards and best practices; lead code reviews and technical design sessions.
• Mentor junior and mid-level engineers through pairing, reviews, and knowledge sharing.
• Produce technical documentation including design docs, runbooks, and Architecture Decision Records (ADRs).

• Bachelor's degree required in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field or equivalent experience required.
• Master's degree strongly preferred in Computer Science, Data Engineering, Data Science, Applied Mathematics, or a related discipline.
• 4-6 years of experience in data engineering or a related field required
• Experience with Big Data technologies required
• 4-6 years of professional experience in data engineering, data platform development, or a closely related data role.
• 3+ years of hands-on experience with Azure Databricks (PySpark, Delta Lake, Workflows, Unity Catalog).
• 3+ years of cloud data engineering experience on Microsoft Azure (ADF, ADLS Gen2, Synapse, Azure SQL, Event Hubs, Key Vault).
• 2+ years of proven experience in SAS-to-cloud migration - translating SAS PROC SQL, macros, and data steps to PySpark or SQL equivalents.
• 2+ years of experience leading Oracle database migration to Azure cloud targets.
• 2+ years of experience working in a regulated financial services environment (banking, capital markets, insurance, asset management, or fintech).
• Demonstrated experience designing and consuming REST APIs and building event-driven integration patterns.
• Hands-on experience with AI/ML data pipeline development, including feature stores, training data pipelines, and MLflow-based workflows.
• Proven track record delivering production-grade data systems in complex enterprise environments.

What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays. Our Culture: Our team members define and shape our culture - an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work - we thrive. Compensation: Competitive pay and bonus eligibility Work Life Balance: Flexible hybrid work environment, minimum of 2-days a week in our Irving, TX office.

Innovation isn't just a talking point at GM Financial, it's how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We're committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.