Databricks Engineer

Cognizant

London, UK

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

dejobs.org

Tech stack

Microsoft Azure Big Data Information Engineering Data Governance Extract Transform Load (ETL) Python (Programming Language) Performance Tuning Azure Data Lake Software Deployment SQL Databases Data Streaming Data Processing

+10 more

Data Ingestion Azure Data Factory Apache Spark Gitlab Data Lakes Pyspark Software Coding Software Version Control Data Pipelines Databricks

Job description

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., * 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.

Requirements

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., * 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.