Key Responsibilities:
Strategic Data Modeling: Translate complex business requirements into efficient, scalable data models and schemas. You will design the logic that turns raw events into actionable business intelligence.
Pipeline Architecture: Design, implement, and maintain resilient data pipelines that serve multiple business domains. You will ensure data flows reliably, securely, and with low latency across our ecosystem.
End-to-End Ownership: Own the data development lifecycle completely-from architectural design and testing to deployment, maintenance, and observability.
Cross-Functional Partnership: Partner closely with Data Analysts, Data Scientists, and Software Engineers to deliver end-to-end data solutions.
What You Bring:
Your Mindset:
Data as a Product: You treat data pipelines and tables with the same rigor as production APIs-reliability, versioning, and uptime matter to you.
Business Acumen: You dont just move data; you understand the business questions behind the query and design solutions that provide answers.
Builders Spirit: You work independently to balance functional needs with non-functional requirements (scale, cost, performance).
Your Experience & Qualifications:
Must Haves:
6+ years of experience as a Data Engineer, BI Developer, or similar role.
Modern Data Stack: Strong hands-on experience with DBT, Snowflake, Databricks, and orchestration tools like Airflow.
SQL & Modeling: Strong proficiency in SQL and deep understanding of data warehousing concepts (Star schema, Snowflake schema).
Data Modeling: Proven experience in data modeling and business logic design for complex domains-building models that are efficient and maintainable.
Modern Workflow: Proven experience leveraging AI assistants to accelerate data engineering tasks.
Bachelors degree in Computer Science, Industrial Engineering, Mathematics, or an equivalent analytical discipline.
Preferred / Bonus:
Cloud Data Warehouses: Experience with BigQuery or Redshift.
Coding Skills: Proficiency in Python for data processing and automation.
Big Data Tech: Familiarity with Spark, Kubernetes, Docker.
BI Integration: Experience serving data to BI tools such as Looker, Tableau, or Superset.


















