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About the role

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At Jacobs, we're challenging today to reinvent tomorrow by solving the world's most critical problems for thriving cities, resilient environments, mission-critical outcomes, operational advancement, scientific discovery and cutting-edge manufacturing, turning abstract ideas into realities that transform the world for good. Your impact Enjoy designing elegant data systems, shipping production-grade code, and seeing your work make a measurable difference, then this role is for you!!

Our team build data platforms and AI solutions that power critical infrastructure, transform operations, and move entire industries. In this role you will work with a broad set of clients on high-scale problems, with the backing of a global organisation, investing heavily in Azure, Databricks, and applied AI. Working primarily on Azure + Databricks (Spark, Delta Lake, Unity Catalog) - Ship modern ELT/ETL, streaming and batch data products, and ML/AI pipelines - Operating at serious scale across water, transport, energy, and more - Join a collaborative, engineering-led culture with real investment in platforms & tooling. Utilising stack such as; Azure ADLS Gen2, Event Hubs, ADF/Azure Data Factory or Synapse pipelines, Functions, Key Vault, VNets Databricks Spark, Delta Lake, Unity Catalog, Workflows, MLflow (experiments, model registry) Languages Python (PySpark), SQL (Delta SQL), optional Scala Engineering Git, pull requests, code review, unit/integration tests, dbx, notebooks as code Platform & Ops Azure DevOps/GitHub, CI/CD, Terraform or Bicep, monitoring/alerting

Requirements

Our team would be delighted to hear from candidates with a good mix of the following skills, experience and attributes.
Utilising SQL and Python for building reliable data pipelines.
Hands-on with Spark (preferably Databricks) and modern data modelling (e.g., Kimball/Inmon/Data Vault, lakehouse). -
Experience running on a cloud data platform (ideally Azure). - Sound software delivery practices: Git, CI/CD, testing, Agile ways of working.
Streaming/event-driven designs (Event Hubs, Kafka, Structured Streaming).
MPP/Data Warehouses (Synapse, Snowflake, Redshift) and NoSQL (Cosmos DB).
ML enablement: feature engineering at scale, MLflow, basic model lifecycle know‑how.
Infrastructure-as-code (Terraform/Bicep) and platform hardening.

Responsibilities

Design & build robust data platforms and pipelines on Azure and Databricks (batch + streaming) using Python/SQL, Spark, Delta Lake, and Data Lakehouse patterns.
Develop AI-enabling foundations: feature stores, ML-ready datasets, and automated model-serving pathways (MLflow, model registries, CI/CD).
Own quality & reliability: testing (dbx/pytest), observability (metrics, logging, lineage), and cost/performance optimisation.
Harden for enterprise: security-by-design, access patterns with Unity Catalog, data governance, and reproducible environments.
Automate the boring stuff: IaC (Terraform/Bicep), CI/CD (Azure DevOps/GitHub Actions), and templated project scaffolding.
Partner with clients: translate business problems into technical plans, run workshops, and present trade-offs with clarity.

About Jacobs

About the Company

Jacobs

Verified

www.jacobs.com/

At Jacobs, we're challenging today to reinvent tomorrow – delivering outcomes and solutions for the world's most complex challenges. With a team of approximately 45,000, we provide end-to-end services in advanced manufacturing, cities & places, energy, environmental, life sciences, transportation and water. From advisory and consulting, feasibility, planning, design, program and lifecycle management, we're creating a more connected and sustainable world.

Engineering

Dallas, TX

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Data Engineer - Databricks / AI

About the Company

Jacobs is hiring · 2 open roles

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