Data Engineer (Databricks)

Who you are

You are a Databricks-focused Data Engineer who understands that great data platforms are only as valuable as the products, AI workflows, and experiences they enable. You bring deep, production-grade expertise across the Databricks platform and know how to connect platform capabilities to real business outcomes.

You thrive in ambiguity and can quickly assess a client's data landscape to recommend and implement the right solutions. You understand that in consulting, your Databricks depth is most valuable when it connects platform capabilities to the products and experiences clients actually use — and you're as comfortable in a product design conversation as you are building a DLT pipeline.

You excel at translating complex data challenges into clear technical requirements and can confidently navigate conversations with everyone from data scientists to executives. Your engineering principles are mature and grounded in real-world experience across various industries and scales.

You have an interest in and a curiosity about data platforms and the latest advances in data technology.

What you will be doing

• Design and build production data pipelines using Lakeflow Declarative Pipelines, Autoloader, and Structured Streaming, with end-to-end ownership of ingestion, transformation, data quality expectations, and CI/CD deployment via Declarative Automation Bundles.
• Architect and implement Lakehouse solutions on Databricks — medallion architecture, Delta Lake, Unity Catalog — tailored to the client's analytics, AI, and application needs.
• Build and maintain Databricks transformation layers — DLT pipelines, PySpark notebooks, and dbt — with data quality constraints and SLAs baked in.
• Design and maintain the data and AI foundations — Unity Catalog, Feature Store, MLflow, and Model Serving — that power production ML, agent workflows, and AI-enabled digital products.
• Collaborate with product and backend engineers to design data models, APIs, and application data contracts — ensuring the platform serves the product, not just the warehouse.
• Consult with clients to understand their data challenges, develop data strategies, and implement sustainable solutions.
• Adapt your approach based on project needs — sometimes leading data architecture discussions with clients, other times supporting internal teams with specialized data expertise.
• Work within multi-cloud environments — primarily AWS and Azure — anchoring data platform recommendations around Databricks where it fits the client's architecture and goals.
• Champion data governance through Unity Catalog — access control, lineage, data quality policies, and compliance — as a first-class part of every engagement, not an afterthought.
• Design data-to-application architectures — including Lakebase-backed services and Databricks Apps — that connect governed data to AI workflows, digital products, and user-facing experiences.
• Help build Livefront's Databricks practice — contributing to accelerators, internal enablement, certification goals, and Databricks partner go-to-market materials alongside delivery work.