Databricks Engineer

Cognizant; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Cognizant in London is seeking an experienced Azure Databricks Data Engineer to design, develop, deploy, and optimize scalable data solutions on Microsoft Azure. You will build production-grade data pipelines with Delta Lake, Unity Catalog, and Databricks tools.

The role requires hands-on expertise in PySpark, Python, SQL, and Spark architecture, plus experience with GitLab-based CI/CD, Databricks Asset Bundles, and Azure Data Lake Storage.

Qualifications

• Hands-on experience with Azure Databricks, Spark, PySpark, Python, and SQL.
• Experience building production-grade data pipelines and governance.
• Experience with Unity Catalog, Delta Lake and Databricks tools.

Responsibilities

• Design, build, test, and maintain scalable data pipelines on Azure Databricks.
• Develop ETL/ELT frameworks for ingestion, transformation, and publishing.
• Manage Delta Tables, schema evolution, data quality, and performance.
• Implement data governance and cataloging using Unity Catalog.
• Create, schedule, monitor, and troubleshoot Databricks workloads and jobs.
• Package and deploy Databricks resources using Asset Bundles and CI/CD.
• Integrate Databricks with ADLS and ADF for secure processing.
• Tune Spark workloads and optimize cost, performance, and reliability.
• Adhere to coding standards and version control; collaborate with teams.

Skills

Azure Databricks

Apache Spark

PySpark

Python

SQL

CI/CD pipelines

GitLab-based CI/CD

Azure DevOps

Tools

Delta Lake

Unity Catalog

Databricks Asset Bundles

GitLab

Azure Data Lake Storage

Azure Data Factory

Azure DevOps

Job description

Role: Databricks Engineer

Location: London (3 days in a week)

Employment: Fulltime

Role Overview

We are seeking an experienced Azure Databricks Data Engineer to design, develop, deploy, and optimize scalable data solutions on Microsoft Azure. The ideal candidate will bring strong hands-on expertise in Azure Databricks, Apache Spark, PySpark, Python, and SQL, with proven experience building production-grade data pipelines, implementing governance through Unity Catalog, and automating deployments using GitLab-based CI/CD and Databricks Asset Bundles.

Key Responsibilities

• Design, build, test, and maintain scalable batch and streaming data pipelines using Azure Databricks, Apache Spark, PySpark, Python, SQL, and Delta Lake.

• Develop reusable ETL and ELT frameworks for data ingestion, transformation, validation, and publishing across lakehouse layers.

• Design and manage Delta Tables, including schema evolution, data quality controls, reliability, and performance optimization.

• Implement data governance, access control, cataloging, and lineage standards using Unity Catalog.

• Create, schedule, monitor, and troubleshoot production workloads using Databricks Jobs and workflows.

• Package and deploy Databricks resources across environments using Databricks Asset Bundles and GitLab-based CI/CD pipelines.

• Integrate Databricks with Azure Data Lake Storage and Azure Data Factory for secure, reliable data processing.

• Optimize Spark workloads, clusters, jobs, and storage patterns for performance, scalability, reliability, and cost efficiency.

• Apply coding standards, version control, testing, documentation, and operational best practices using GitLab and Azure DevOps.

• Collaborate with architects, analysts, and engineering teams to translate business requirements into maintainable technical solutions.

Required Skills and Experience

• Strong hands-on experience with Azure Databricks and the Apache Spark execution architecture.

• Advanced proficiency in PySpark, Python, and SQL for large-scale data processing and transformation.

• Practical experience with Delta Lake, Delta Tables, Unity Catalog, Databricks Jobs, and Databricks Asset Bundles.

• Proven experience designing and operating production-grade ETL or ELT pipelines on Azure.

• Hands-on experience implementing CI/CD pipelines using GitLab, including automated validation and multi-environment deployments.

• Working knowledge of Azure Data Lake Storage, Azure Data Factory, and Azure DevOps.

• Demonstrated ability to tune Spark workloads and troubleshoot data pipeline performance and production issues.

• Strong understanding of data engineering, governance, security, version control, testing, and deployment best practices.

Preferred Qualifications

• Databricks Certified Data Engineer Associate or Professional certification.

• Microsoft Azure data engineering certification or equivalent cloud certification.

• Experience with medallion architecture, data quality frameworks, streaming pipelines, infrastructure as code, or lakehouse monitoring.

• Exposure to enterprise data governance, regulated environments, or large-scale cloud data modernization programs.