Data Engineer

Everything we deliver runs on trustworthy data. That's the piece you'd own. Client data is fragmented and rarely where it needs to be. Your work is to turn it into a foundation the rest of the team can build on without a second thought. When a data scientist ships a model or an AI engineer deploys an agent, they rely on the pipelines you designed. Get this right and everything above it stands. That's the kind of work that doesn't always get applause, but everyone feels it when it's done well. **The work:** * Design and run reliable pipelines at scale, batch and streaming * Architect the data platforms (warehouses, lakes, lakehouses) and the graph data foundation behind our GraphRAG work * Own data quality, governance, lineage, and access * Model data to serve both operational and analytical use, and stand up the feature-store infrastructure the data scientists draw on * Serve clean, documented data through clear contracts, and build reusable foundations so each engagement starts faster than the last **Where your work ends.** You own data up to the contract: getting it in, making it trustworthy, and serving it. You don't build the models that use it (that's the Data Scientist) or the model-serving platform (AI Engineer and Software Engineer). Your job is to make sure no one downstream ever has to wonder whether the data is right. **What success looks like:** * The team trusts your data enough to build on it without re-checking it * Pipelines run reliably and tell you when something breaks * Every data consumer has a clear, documented contract to work against * Foundations you build get reused across engagements instead of rebuilt The stack we work in today: Python for the pipeline work; Postgres, MongoDB, and Neo4j across relational, document, and graph data; PySpark and HDFS for scale; Airflow for orchestration; Databricks as a platform; pipelines running in containers with the observability to know when one breaks; and AWS, GCP, or Azure underneath. You don't need every one of these. You do need the three shared foundations: software discipline, MLOps, and systems thinking. **About The Strong AI, and how we work** The Strong AI is an end-to-end AI implementation partner. Clients come to us because most organizations can run an AI experiment, but few can turn it into a system their business depends on. We close that gap. We don't hand over slideware or a notebook; we build systems that work inside a client's business, and where they want it, we run them. You'll work across engagements and industries, on different problems and often different stacks. We're technology-agnostic: the problem and the client's environment choose the tools, so treat any stack we list as the ground we work on today, not a gate. **Across all roles, we ask for the same way of working:** * Real software. Tested, reviewed, versioned code the next person, or the client's team, can pick up. * MLOps mindset. A model's life starts at deployment. Monitoring, retraining, drift, and rollback are handled before anything breaks. * Systems thinking. You see both the value slice and the whole it compounds into. * Quality and security, owned by you. Designed in from the first decision, not inspected in at the end. Everyone builds to the highest standard. * Built for handover. Clear code and docs the client's own team can understand, operate, and take over.