Build, test, deploy, and support reliable data pipelines, data products, and datasets that support reporting, analytics, AI/ML, and business operations. Work from architecture and business requirements to create production-ready solutions that are scalable, secure, cost efficient, and easy to support. Use approved AI tools to speed up coding, testing, troubleshooting, and documentation, and validate all generated work before production use.

·       Build, test, deploy, and support scalable data pipelines and datasets using Databricks, Spark, SQL, Python/PySpark, dbt, Airflow, or similar tools.

·       Support batch and streaming data workloads, including ingestion, transformation, and consumption patterns for analytics and downstream systems.

·       Build reliable data products for reporting, analytics, AI/ML, and business operations based on business and architecture requirements.

·       Engineer Bronze, Silver, and Gold data layers in Databricks, following medallion architecture patterns where applicable.

·       Build and maintain transformation pipelines using dbt, Spark-based frameworks, and approved engineering standards.

·       Document solutions and improve pipeline performance, storage use, compute efficiency, and total cost to operate.

·       Add automated tests, data-quality checks, monitoring, alerts, and clear error handling to improve data freshness, accuracy, and pipeline reliability.

·       Troubleshoot production issues, find the cause, and implement lasting fixes that reduce repeat incidents.

·       Support service levels and production readiness by making pipelines observable, testable, and easy to operate.

·       Use source control, code review, CI/CD, and release practices.

·       Participate in design and code reviews and share practical engineering practices with other engineers.

·       Support semantic layers, aggregates, curated datasets, and performance improvements that help business users access trusted data.

·       Partner with architecture, analytics, and product teams to make data products easier to use, reuse, and support.

·       Support platforms such as Dataiku, Databricks, and Power BI where they are used for analytics and self-service delivery.

·       Follow access, encryption, privacy, retention, metadata, lineage, and stewardship requirements.

·       Build integrations across internal systems, external data sources, and cloud platforms using secure and reliable patterns.

·       Handle data in ways that meet company security, privacy, compliance, and audit expectations.

·       Work with Security, Privacy, Governance, Architecture, and Platform teams when standards are unclear.

·       Use approved AI tools, including Windsurf, Devin, and Databricks Genie where appropriate, to speed up coding, testing, debugging, documentation, data analysis, and routine engineering work.

·       Give AI tools clear context and requirements so generated code and recommendations fit the problem being solved.

·       Review, test, and understand AI-generated code before it is merged, deployed, or used in production.

·       Do not place restricted data, credentials, or confidential information into tools that are not approved for that use.

·       Follow company security, privacy, architecture, code-review, and responsible AI standards.

·       Share useful AI-assisted practices with the team and identify work that can be simplified or automated.

·       Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent experience.

·       Strong hands-on experience with the tools and practices used by the role.

·       Experience testing, documenting, and supporting production solutions.

·       Experience using AI-assisted development tools and validating AI-generated work.

·       5+ years in data engineering.

·       Experience with streaming, MDM, BI, semantic layers, or AI/ML data.

·       Experience building data products or curated data layers using Databricks medallion architecture.

·       Experience with observability, data quality tooling, performance tuning, and cost optimization for cloud data workloads.

·       Experience mentoring other engineers through code reviews, design reviews, or day-to-day technical guidance.

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