Arionkoder is an AI consulting firm helping companies build, embed, and own AI systems that drive real business outcomes. We combine Product Development, Artificial Intelligence, and Team Augmentation to craft digital products and solutions that create lasting impact, guided by a human-centric approach and a commitment to pushing the boundaries of what technology can do.

We’re hiring a Freelance Data Engineer to build the structured core of a governed data foundation on Databricks, used by AI agents, applications, and business teams. You own everything that has to be exact: business entities reconciled across systems that use different identifiers, facts with their source and validity period, business definitions turned into certified tables, access control, and the APIs and MCP tools that serve this data with the caller’s permissions.

Conflicting definitions and identifiers are business problems as much as joins. The right person has spent years on Databricks making data correct and consistent, has enforced access control in production, and knows where platform controls stop and application-level enforcement has to start. You work autonomously with business stakeholders and ask the uncomfortable questions about definitions and permissions early.

You’ll work in a small team (solution architect, AI engineer, full-stack engineer, data quality lead, product and project managers) alongside the client’s data platform team. You follow their conventions and hand over what you build. The AI engineer owns the parts that need a model: knowledge from documents, retrieval, agent behavior, and evals. You give them the trusted data those parts depend on. AI tools speed up your modeling, SQL, testing, and documentation every day, and you know exactly when to trust the output and when not to.

Required Qualifications

• 6+ years of professional data engineering, building production-grade pipelines and data models
• 3+ years hands-on with Databricks in production, including Unity Catalog
• Expert SQL and Python: writes, debugs, optimizes, and refactors pipelines unaided. Spark/PySpark a plus
• Strong data modeling, including history-aware techniques (SCD Type 2, effective dating, temporal validity), with provenance and lineage designed in from the start
• Data quality and testing discipline: tests and expectations in pipelines (dbt tests, Great Expectations, or similar) and CI/CD for data
• Reconciling data across systems: entity resolution or master data
• Incremental and change-driven processing (CDC, streaming, or incremental frameworks) against freshness targets
• Practical data governance: has implemented access control in production (row-level security, column masking, or role/attribute-based access) and knows where platform-level controls stop and application-level enforcement has to start
• Has built APIs or services that serve data
• Senior judgment: works autonomously, challenges unclear definitions, prioritizes by risk, owns outcomes without micromanagement
• Daily use of AI coding tools (Claude, Cursor, or similar), with the judgment to know when to trust the output
• C1 English; comfortable working fully remote and directly with a client team

Preferrable:

• Exposing data to applications through APIs or MCPs
• Multi-tenant data isolation for customer-facing products, with security and negative testing
• Finance or CRM data (NetSuite, Salesforce)
• Consolidating data across acquired companies or business units
• Ingesting and governing unstructured content (documents, wikis, contracts) alongside structured data
• Turning business definitions into a shared semantic or metrics layer with business or finance stakeholders
• FinOps / cost attribution on data platforms