Staff AI & Data Engineer – Databricks

Eliassen Group; United States; Remote; USD 110,000 - 124,000; Full time

Job summary

Eliassen Group is seeking a highly experienced Staff AI & Data Engineer to design and build enterprise-grade data and AI solutions on Databricks. This hands-on role strengthens the Databricks platform, onboarding new data sources, and creating trusted Gold-layer data products.

You will develop semantic layers, AI agents, and governance, working across pipelines, data quality, and cross-domain analytics, with a focus on experimental AI readiness and scalable architectures.

Qualifications

• Advanced hands-on experience with Databricks, Apache Spark, Delta Lake, and SQL.
• Hands-on experience with AWS services related to data analytics and AI, including EMR and Bedrock.
• Strong Python and SQL development skills.
• Experience building scalable batch and/or streaming data pipelines.
• Deep understanding of Bronze/Silver/Gold medallion architectures.
• Experience with Databricks Lakeflow or comparable orchestration technologies.
• Experience implementing enterprise data quality, lineage, observability, and monitoring.
• Experience with Unity Catalog and governed enterprise data environments.
• Strong understanding of dimensional modeling, data products, semantic models, and cross-domain integration.
• Experience with Git-based development, CI/CD, automated testing, and production deployment.
• Ability to troubleshoot complex distributed data and pipeline issues.
• Experience building AI and GenAI solutions against enterprise data.
• Understanding of semantic layers and natural-language-to-data architectures.
• Experience with Databricks Genie or comparable natural-language analytics technology.
• Experience building tools, functions, APIs, or services consumed by AI agents.
• Understanding of MCP, agent tool calling, tracing, evaluation, and AI governance.
• Experience developing production AI solutions with security, observability, and governance controls.

Responsibilities

• Design and build production-grade data pipelines on Databricks.
• Ingest data from enterprise applications, databases, APIs, SaaS platforms, files, and other source systems.
• Build and maintain Bronze, Silver, and Gold medallion architectures.
• Implement incremental processing and change data capture patterns where appropriate.
• Standardize types, codes, units, keys, and business definitions across source systems.
• Implement deduplication, historical tracking, schema evolution, and data lineage.
• Optimize pipelines for reliability, performance, scalability, and cost.
• Build a configuration-driven source onboarding framework and Lakeflow pipeline templates.
• Automate source registration, deployment, monitoring, lineage, and operational controls.
• Implement data quality expectations, anomaly detection, monitoring, and quarantine processes.
• Implement job and pipeline monitoring, alerting, audit logging, and operational dashboards.
• Support automated deployment and promotion using Databricks Asset Bundles and CI/CD.
• Develop conformed dimensions and shared-key strategies across domains.
• Build integrated Gold-layer products and reusable data contracts.
• Build AI-ready Gold data products with ownership, lineage, definitions, and quality information.
• Develop semantic context, business glossary, ontology, and certified KPIs in Unity Catalog.
• Build governed Unity Catalog functions and agent-ready tools and endpoints.
• Implement tracing, evaluation, testing, monitoring, and governance for AI and agent workflows.
• Support development of AI agents that use trusted enterprise data.

Skills

Databricks

Apache Spark

Delta Lake

SQL

Python

AWS

CI/CD

Git

Data pipelines

GenAI

Tools

Databricks Lakeflow

Unity Catalog

MLflow

Bedrock

EMR

Databricks Genie

Job description

Description

Remote

Our client is seeking a highly experienced Staff AI & Data Engineer to design and build enterprise-grade data and AI solutions on Databricks. This hands‑on role focuses on strengthening the Databricks platform, onboarding new data sources, building reusable ingestion and transformation frameworks, creating trusted Gold‑layer data products, enabling cross‑domain analytics, and developing semantic and agent‑ready capabilities. The ideal candidate brings strong data engineering expertise with modern AI and GenAI engineering skills and works across pipelines, modeling, data quality, governance, semantic layers, natural‑language analytics, and AI agents.

Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $80.00 to $90.00/hr. w2

JN -112026-109353

Responsibilities

• Design and build production‑grade data pipelines on Databricks.
• Ingest data from enterprise applications, databases, APIs, SaaS platforms, files, and other source systems.
• Build and maintain Bronze, Silver, and Gold medallion architectures.
• Implement incremental processing and change data capture patterns where appropriate.
• Standardize types, codes, units, keys, and business definitions across source systems.
• Implement deduplication, historical tracking, schema evolution, and data lineage.
• Optimize pipelines for reliability, performance, scalability, and cost.
• Build a configuration‑driven source onboarding framework and Lakeflow pipeline templates.
• Automate source registration, deployment, monitoring, lineage, and operational controls.
• Implement data quality expectations, anomaly detection, monitoring, and quarantine processes.
• Implement job and pipeline monitoring, alerting, audit logging, and operational dashboards.
• Support automated deployment and promotion using Databricks Asset Bundles and CI/CD.
• Develop conformed dimensions and shared‑key strategies across domains.
• Build integrated Gold‑layer products and reusable data contracts.
• Build AI‑ready Gold data products with ownership, lineage, definitions, and quality information.
• Develop semantic context, business glossary, ontology, and certified KPIs in Unity Catalog.
• Build governed Unity Catalog functions and agent‑ready tools and endpoints.
• Implement tracing, evaluation, testing, monitoring, and governance for AI and agent workflows.
• Support development of AI agents that use trusted enterprise data.

Experience Requirements

• Advanced hands‑on experience with Databricks, Apache Spark, Delta Lake, and SQL.
• Hands‑on experience with AWS services related to data analytics and AI, including EMR and Bedrock.
• Strong Python and SQL development skills.
• Experience building scalable batch and/or streaming data pipelines.
• Deep understanding of Bronze/Silver/Gold medallion architectures.
• Experience with Databricks Lakeflow or comparable orchestration technologies.
• Experience implementing enterprise data quality, lineage, observability, and monitoring.
• Experience with Unity Catalog and governed enterprise data environments.
• Strong understanding of dimensional modeling, data products, semantic models, and cross‑domain integration.
• Experience with Git‑based development, CI/CD, automated testing, and production deployment.
• Ability to troubleshoot complex distributed data and pipeline issues.
• Experience building AI and GenAI solutions against enterprise data.
• Understanding of semantic layers and natural‑language‑to‑data architectures.
• Experience with Databricks Genie or comparable natural‑language analytics technology.
• Experience building tools, functions, APIs, or services consumed by AI agents.
• Understanding of MCP, agent tool calling, tracing, evaluation, and AI governance.
• Experience developing production AI solutions with security, observability, and governance controls.
• Preferred: Databricks Data Engineer Professional or equivalent experience.
• Preferred: Experience with Databricks AI/BI, Genie, MLflow, and MCP‑based integrations.
• Preferred: Experience in Real Estate, Finance, People/Workplace, Operations, or similar domains.
• Preferred: Experience building metadata‑driven or configuration‑driven ingestion frameworks.