Azure Databricks Engineer

Opus Recruitment Solutions
London, UK
11 days ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Compensation
£91,000.0 - £104,000.0
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) Microsoft Azure Cloud Computing Continuous Integration Information Engineering Data Infrastructure Data Integration Data Integrity Extract Transform Load (ETL) Data Transformation Database Queries DevOps
+7 more
Python (Programming Language) SQL Databases Apache Spark Pyspark Data Management Data Pipelines Databricks

Job description

I’m currently working with a client who is looking for an experienced Azure Databricks Engineer to work 3 days on site in London. You will join a data transformation programme. The role will involve designing and optimising data pipelines, integrating multiple data sources, and ensuring the reliability and scalability of the data platform., * Design, develop, and optimize data pipelines using Azure Databricks.

  • Ensure data reliability, scalability, and quality.
  • Integrate data sources into a unified platform.
  • Monitor and troubleshoot data workflows.

Requirements

  • Expertise in Azure Databricks, Spark, and big data tools.
  • Strong SQL skills
  • Knowledge of Python, ETL processes and cloud technologies.
  • Familiarity with CI/CD practices for data pipelines.
  • Experience within the insurance sector would be highly beneficial, * Communication Skills (Key)
  • Data Integration (Intern)
  • Data Quality
  • Databricks (Data Engineering)
  • DevOps Engineering (Java)
  • Health and Safety Principles
  • Microsoft Azure (Line)
  • PySpark
  • Python Programming (Beginner)
  • SQL Databases

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:05 min

Enhancing Databricks tooling for software engineering workflows

Alan Mazankiewicz · LIVE

5:14 min

Executing Databricks jobs with built-in Airflow operators

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Harnessing Spark with Python using PySpark and Py4J

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Analyzing differences between mobile and traditional backend DevOps

Mete Baydar Mete Baydar · World Congress 2025

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Overview of Databricks and interactive data processing

Alan Mazankiewicz · LIVE

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Building generic custom operators for Databricks APIs

Alan Mazankiewicz · LIVE

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