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A VC-backed retail AI scale-up is expanding its engineering team and is looking for a Staff Data Engineer with deep Spark expertise to help architect and scale the data backbone behind cutting-edge AI-driven systems.
Job Responsibility:
Design and evolve distributed, cloud-based data infrastructure that supports both real-time and batch processing at scale
Build high-performance data pipelines that power analytics, AI/ML workloads, and integrations with third-party platforms
Champion data reliability, quality, and observability, introducing automation and monitoring across pipelines
Collaborate closely with engineering, product, and AI teams to deliver data solutions for business-critical initiatives
Requirements:
5+ years in software development and data engineering with ownership of production-grade systems
Proven expertise in Spark (Databricks, EMR, or similar) and scaling it in production
Strong knowledge of distributed computing and modern data modeling approaches
Solid programming skills in Python, with an emphasis on clean, maintainable code
Hands-on experience with SQL and NoSQL databases (e.g., PostgreSQL, DynamoDB, Cassandra)
Excellent communicator who can influence and partner across teams
Nice to have:
Experience in high-growth, early-stage environments
Familiarity with MLOps and deploying ML models into production data workflows
A problem-solver at heart, excited by innovation and complex challenges