Data Engineer

• Experience: 6+ years of experience in data engineering, backend engineering, or a highly analytical data infrastructure role.
• SQL & Warehouse Mastery: Exceptional SQL skills with a deep understanding of database internals, query execution plans, and analytical performance tuning.
• dbt Expertise: Proven track record of deploying and managing dbt in production at scale, including experience with dbt packages, custom macros, and documentation generation.
• Software Engineering Mindset: Strong Python development skills, including writing reusable modules, handling APIs, using virtual environments, and implementing unit tests.
• Cloud-Native Fluency: Deep familiarity with cloud environments (AWS, GCP, or Azure) and containerized workflows (Docker).
• Communication Skills: Ability to clearly explain data architecture choices, trade-offs, and data lineages to both technical and non-technical stakeholders.

Job responsibilities

• Data Pipeline Development: Design, build, and maintain scalable ELT/ETL pipelines using Python and SQL to ingest data from internal applications, production databases, and third-party APIs.
• Data Transformation Architecture: Own the dbt environment, establishing best practices for model layering (staging, intermediate, marts), macro development, and state management.
• Warehouse Optimization: Architect and optimize schemas within our cloud data warehouse, ensuring efficient indexing, partitioning, clustering, and cost-effective query execution.
• Data Quality & Testing: Implement rigorous data testing, observability, and monitoring frameworks (using dbt tests, Great Expectations, or similar tools) to ensure data trustworthiness and uptime.
• Workflow Orchestration: Schedule and monitor complex, dependency-aware data workflows using modern orchestrators (e.g., Airflow, Dagster).
  • Collaboration & Enablement: Partner with Analytics Engineers, Data Scientists, and Product teams to translate complex business requirements into clean, well-documented data models.