Data Platform Engineer (Warehouse + Lakehouse + Analytics)

hands-on Data Platform Engineer

• Hands-on Architecture and Development:

  Define, design, prototype, and deliver a robust lakehouse and analytics platform leveraging best-in-class technologies.

• Build the Core Platform:

  Collaborate with the Staff Data Architect to set up core infrastructure involving ingestion (ETL/ELT), warehouse, transformation, orchestration, and BI/analytics layers.

• Leverage Modern Data Stack:

  Work across an environment involving GCP-based compute instances and containers (Airbyte, ClickHouse, dbt, metriql, Prefect, Metabase, Next.js frontend) bringing cutting-edge engineering principles to life.

• Optimize for Scale and Security:

  Implement warehouse best practices, optimize performance for large-scale analytics, and enforce role- and attribute-based access controls.

• Data Modeling Excellence:

  Design flexible and scalable models to support real-time analytics, complex business queries, and long-term historical analysis.

• Innovation and Automation:

  Drive automation wherever possible in data ingestion, validation, transformation, and reporting processes.

• Cross-functional Collaboration:

  Work closely with Product, Engineering, and Leadership teams to ensure alignment with broader company objectives and business strategies.

• Mentor and Uplift:

  Provide technical mentorship to junior engineers and set high standards for operational excellence across the data organization.

• Drive Success Metrics:

  Align milestone progress with leadership goals around platform adoption, scalability, reliability, and impact on business KPIs.

• 5+ years of experience designing, building, and scaling modern data platforms.
• Proven hands-on expertise with ETL/ELT pipelines, cloud-based warehouses (SQL, NoSQL, MPP, OLAP/OLTP), and advanced database optimization.
• Strong SQL and Python skills for data engineering and automation.
• Familiarity with setting up containerized data services (Docker/Kubernetes environments).
• Deep understanding of cloud platforms at least one of GCP, AWS, or Azure.
• Exposure to analytics workflows and BI environments.
• Strong experience working with Git, Jira, Confluence, Jenkins, and modern DevOps pipelines.