Senior Data Product Engineer, Enabling Functions

Role Summary

Senior Data Product Engineer with deep expertise in DBT, Databricks, Power BI/Tableau and AWS, who can architect and implement scalable, reliable, and high-performance data products. Design and deliver production-grade data pipelines and models, optimize query and compute performance, and expose data to end users through well-structured semantic layers and dashboards.

Responsibilities

• Architect and implement end-to-end ELT workflows with DBT (core and Cloud), ensuring modular, testable, and reusable transformations.
• Build high-performance data pipelines in Databricks (PySpark, Delta Lake, Unity Catalog) for batch and streaming workloads.
• Engineer scalable data ingestion pipelines into AWS (S3, Kinesis, Glue, Lambda, Step Functions) with strong monitoring and fault tolerance. Ensure observability, cost efficiency & scalability in all pipeline and compute designs.
• Design normalized and star-schema models for analytical workloads, following dbt’s best practices and software engineering principles.
• Implement data quality testing frameworks (dbt tests, Great Expectations, or custom validations) with automated CI/CD integration.
• Manage data versioning, lineage, and governance through tools such as Unity Catalog and AWS Lake Formation.
• Develop semantic data layers that support self-service analytics across BI tools (Tableau, Power BI etc.).
• Build interactive, real-time dashboards with metric consistency and role-based access control.
• Partner with analysts and data scientists to optimize queries and deliver production-ready datasets.
• Automate deployment pipelines with CI/CD (GitHub Actions, GitLab CI, or AWS CodePipeline) for dbt and Databricks.
• Implement infrastructure-as-code (IaaC) for reproducibility (Terraform, CloudFormation).
• Ensure system reliability through observability and monitoring (Datadog, CloudWatch, Prometheus, or similar).
• Benchmark and optimize SQL, Spark, and BI query performance at scale.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• 8+ years in data engineering, analytics engineering, or data platform development.
• Expert-level proficiency in DBT: advanced macros, Jinja, testing, exposures, dbt Cloud deployment.
• Databricks: Spark (PySpark, SQL), Delta Lake, Unity Catalog.
• AWS: S3, Glue, Lambda, Step Functions, Datasync, EMR, Redshift, IAM and networking/security fundamentals.
• Data Visualization: Power BI or Tableau.
• Strong programming in Python and SQL (including query optimization).
• Experience with distributed systems and large-scale datasets (TB–PB scale).
• Experience implementing CI/CD pipelines, data testing, and infrastructure as code.
• Solid understanding of data governance, security, and compliance in enterprise environments.

Preferred Skills

• Experience with real-time data pipelines (Kafka, Kinesis, Delta Live Tables).
• Familiarity with containerization and orchestration (Docker, Kubernetes, EKS).
• Exposure to machine learning workflows in Databricks.

Education

• Listed in Qualifications above.

Additional Requirements

• Location: US-based candidates (salary band provided in the description).