We're looking for a Data Engineer to own our data architecture end-to-end. You'll design, build, and scale the pipelines that power every pricing decision we make - from raw ingestion through our data lake to the analytics and BI layers our customers rely on. This is a hands-on leadership role: you'll set technical direction, drive architectural decisions, and mentor a team of data engineers, while still writing code and owning delivery.What You'll Do:Architect and build large-scale batch and streaming ETL/ELT pipelinesOwn data lake design, table modeling, and partitioning strategy across billion-row datasetsDrive query performance and cloud cost optimization across AWS and GCPEstablish data quality, observability, and reliability standards - freshness, correctness, and SLAsPartner with backend, product, and data science teams to expose data through internal and customer-facing APIsOur StackSpark / EMR Airflow BigQuery PostgreSQL & Aurora Kafka RabbitMQ Elasticsearch Go Python AWS GCP Kubernetes Terraform.Requirements: 5 years in data engineering, with real production experience at scale (terabytes , billions of rows)Deep SQL and strong distributed-processing experience (Spark or equivalent)Strong Python and/or GoHands-on experience with cloud data warehouses (BigQuery, Snowflake, Redshift) and orchestration toolingAI-first mindset - you actively work with AI coding tools (Claude Code, Cursor, Copilot) and LLM-based agents as part of your day-to-day, and look for opportunities to automate and accelerate engineering work with themExperience building or supporting AI/LLM-driven data products - pipelines that feed models, agents, or ML systemsProduct mindset: you care why the data is being used, not just that the job finished greenNice to have: streaming architectures, cost/FinOps ownership, multi-region or multi-cloud systems, e-commerce or pricing domain experience, MCP servers or agentic tooling.This position is open to all candidates.
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