{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Data Engineer – Databricks / AI”, “description”: “
Job Overview
At Jacobs, we challenge today to reinvent tomorrow by solving critical problems for thriving cities, resilient environments, and mission‑critical outcomes. Our data engineering team builds data platforms and AI solutions that power critical infrastructure and transform operations. In this role you will design, build, and ship elegant data systems on Azure and Databricks, working with Azure ADLS, Data Factory, Event Hubs, Databricks, and related Azure services.
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: Unity Catalog access patterns, data governance, and reproducible environments.
- Automate infrastructure and deployment with IaC (Terraform/Bicep), CI/CD (Azure DevOps/GitHub Actions), and templated project scaffolding.
- Partner with clients to translate business problems into technical plans, run workshops, and present trade‑offs with clarity.
- Ship value continuously, iterate, review, release frequently, and measure outcomes rather than only outputs.
Qualifications
- Proficiency in SQL and Python for building reliable data pipelines.
- Hands‑on experience with Spark (preferably Databricks) and modern data modelling (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.
- Knowledge of streaming/event‑driven designs (Event Hubs, Kafka, Structured Streaming).
- Experience with MPP/ data warehouses (Synapse, Snowflake, Redshift) and NoSQL (Cosmos DB).
- ML enablement: feature engineering at scale, MLflow, and basic model lifecycle knowledge.
- Infrastructure‑as‑code (Terraform/Bicep) and platform hardening experience.
EEO and Accessibility Statement
- We are an equal opportunity employer and a disability‑confident employer, welcoming candidates who may require reasonable adjustments.
- We welcome applications from those seeking flexible working and from applicants who may not meet all listed requirements.
- All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, national origins, age, disability, or any other protected characteristic.
Company: Jacobs
Location: Bristol
Job Description:
Job Overview
At Jacobs, we challenge today to reinvent tomorrow by solving critical problems for thriving cities, resilient environments, and mission‑critical outcomes. Our data engineering team builds data platforms and AI solutions that power critical infrastructure and transform operations. In this role you will design, build, and ship elegant data systems on Azure and Databricks, working with Azure ADLS, Data Factory, Event Hubs, Databricks, and related Azure services.
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: Unity Catalog access patterns, data governance, and reproducible environments.
- Automate infrastructure and deployment with IaC (Terraform/Bicep), CI/CD (Azure DevOps/GitHub Actions), and templated project scaffolding.
- Partner with clients to translate business problems into technical plans, run workshops, and present trade‑offs with clarity.
- Ship value continuously, iterate, review, release frequently, and measure outcomes rather than only outputs.
Qualifications
- Proficiency in SQL and Python for building reliable data pipelines.
- Hands‑on experience with Spark (preferably Databricks) and modern data modelling (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.
- Knowledge of streaming/event‑driven designs (Event Hubs, Kafka, Structured Streaming).
- Experience with MPP/ data warehouses (Synapse, Snowflake, Redshift) and NoSQL (Cosmos DB).
- ML enablement: feature engineering at scale, MLflow, and basic model lifecycle knowledge.
- Infrastructure‑as‑code (Terraform/Bicep) and platform hardening experience.
EEO and Accessibility Statement
- We are an equal opportunity employer and a disability‑confident employer, welcoming candidates who may require reasonable adjustments.
- We welcome applications from those seeking flexible working and from applicants who may not meet all listed requirements.
- All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, national origins, age, disability, or any other protected characteristic.
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Posted: May 20th, 2026