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Senior Data Engineer

Remote

Job description

We are looking for a Senior Data Engineer to build and run the pipelines and platforms that move our customers' data: from source systems to the warehouse, on production systems with real consequences.

We’re looking for someone who loves to build, learn, and guide others. You’ll be working closely with our leadership (CEO/COO) and our clients, so clear communication and a collaborative spirit are key.

How You'll Contribute

  • Pipeline Engineering: Build and operate robust ingestion pipelines (both batch and streaming) from databases, event streams, and third-party APIs.
  • Storage & Performance: Own storage architecture across the lake, lakehouse, and warehouse, optimizing for partitioning, cost efficiency, and query speed.
  • Reliability & Observability: Keep pipelines observable and restartable by implementing orchestration, automated testing, alerting, and ensuring clean recovery when something breaks at 08:00.
  • Engineering Best Practices: Work like a true software engineer by implementing infrastructure as code, CI/CD for data workloads, code reviews, and strict version control.
  • Data Contracts & Governance: Agree on data contracts with data-producing teams to keep schema drift under control, while managing access, retention, and lineage.
  • Lasting Documentation: Produce clear and comprehensive documentation that ensures your work outlives the engagement.

We're Looking for Someone Who...

  • Has Proven Production Experience: You have built and ran data pipelines in production—and, more importantly, you can articulate what you learned when they broke.
  • Is an AI-Augmented Engineer: You seamlessly integrate AI tooling into your daily workflow, but you maintain the strict review discipline to verify what it generates before it ships.
  • Is a Confident Communicator: You possess fluent English and are completely comfortable working directly with customer teams.
  • Has a Cost Engineering Mindset (Valued): You know how to make a pipeline cheaper and can quantify the savings.
  • Understands Compliance (Valued): You have exposure to regulated environments, including GDPR obligations, access control, and auditable lineage.


Requirements


  • Core Languages: Advanced, production-grade SQL and Python.
  • Cloud Platforms: Production depth in one major cloud provider (AWS, GCP, or Azure), with a solid grasp of principles to move between them. (A second cloud platform is a plus).
  • Orchestration: Production experience with an orchestrator from a main vendor (e.g., Airflow, Dagster, Azure Data Factory, or equivalent).
  • Warehousing & Transformation: Warehouse depth in Snowflake or BigQuery (dimensional or Data Vault modelling), and experience with dbt or equivalent transformation frameworks.
  • Vast experience with Snowflake & Terraform.
Nice to have
  • Processing & Streaming: Spark or PySpark at scale, Databricks, and streaming/pub-sub brokers like Kafka, Kinesis, Pub/Sub, or MQTT-class brokers.
  • AI Data Infrastructure & Formats; Embedding pipelines, vector stores, retrieval feeds, feature stores, and open table/file formats (Parquet, Iceberg, Delta).
  • DevOps & Quality: , Docker, Kubernetes, GitHub Actions, and data quality frameworks in production (Great Expectations, Soda, Monte Carlo).
  • Source Systems: NoSQL stores (like MongoDB) and marketing/ad-platform data (Google Ads, Meta, CRM platform APIs).


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