Data Engineer Azure Data Platform

About Synextra Synextra is a Microsoft-specialist Managed Service Provider headquartered in Warrington, operating as a premium partner to regulated mid-market organisations including law firms, financial services firms, and mortgage lenders. We're deliberately small - around 35 people - because we believe the best outcomes come from technical depth, not headcount. Our AI Services Division is growing fast, and we're building out a serious data and engineering capability to match. This is a chance to get in early and shape how that function operates.

The Role We're looking for a technically driven Azure Data Engineer to join our data platform team. You'll design, build, and maintain production-grade data pipelines on Microsoft Azure - transforming complex, diverse datasets into analytics-ready formats that power business intelligence and AI initiatives for our clients and internally.The ideal candidate treats pipelines and infrastructure as code, with a genuine passion for software engineering in a data context. You'll work across the modern Azure data stack - ADF, ADLS Gen2, PySpark, Delta Lake - with increasing exposure to Microsoft Fabric as the platform matures. You'll collaborate closely with customers and internal teams to ensure data is structured and governed for reliable downstream consumption.This is a hands-on engineering role with room to grow into leadership: you'll champion DevOps best practices, contribute to architectural decisions, and help mentor junior engineers as the team scales.

Responsibilities

• Architect and write production-grade ELT/ETL data pipelines using PySpark and Python within Azure ecosystem.

• Build custom, reusable data processing frameworks and libraries in Python/Scala to streamline ingestion and transformation tasks across the engineering team

• Programmatically ingest large volumes of structured and unstructured data from REST APIs, streaming platforms (e.g. Event Hubs, Kafka), and legacy databases into ADLS Gen2 and OneLake

• Develop structured data models aligned to Lakehouse, Medallion Architecture, and Delta Lake patterns

• Continuously profile, debug, and optimise Spark jobs, SQL queries, and Python scripts for maximum performance and cost-efficiency at scale

• Champion DevOps best practices: implement infrastructure-as-code (Terraform), automated testing, and CI/CD deployment pipelines via Git and Azure DevOps

• Identifying patterns in recurring issues and engineering permanent solutions

• Write comprehensive unit and integration tests for all data pipelines to ensure data integrity; enforce data governance protocols, RBAC, and encryption standards across all environments

Requirements

Essential Technical Skills

• Advanced proficiency in Python and PySpark, writing clean, modular, object-oriented code for data transformations

• Strong command of SQL (T-SQL, Spark SQL) for data exploration, validation, and final-stage modelling

• Deep hands-on experience with Microsoft Fabric and its tooling such as Azure Data Factory (ADF), and Azure Data Lake Storage (ADLS Gen2)

• Practical experience with Git, branching strategies, automated testing (e.g. pytest), and CI/CD orchestration via Azure DevOps

• Proven commercial track record of deploying complex data solutions on the Microsoft Azure platform

• Experience collaborating with a range of stakeholders to structure data for downstream consumption (e.g. MLflow, Power BI semantic models)

• Infrastructure-as-code experience with Terraform for Azure resource provisioning

Desirable Technical Skills

• Familiarity with streaming data architectures (Spark Structured Streaming)

• Knowledge of complementary modern data stack tools such as dbt for SQL-based transformations

• Experience integrating Large Language Models (LLMs) or operationalising AI/ML models

Personal Qualities

• Exceptional problem-solving abilities and a persistent, detail-oriented approach to debugging complex code

• Strong communication skills to effectively translate business requirements into technical architectures

• A proactive mindset focused on continuous learning and staying ahead of the rapidly evolving data landscape

• Willingness to review code submissions, enforce coding standards, and mentor junior engineers on the team

Preferred Background

• 3–5+ years in software engineering, data engineering, or Big Data environments with a code-first approach

• Proven commercial experience deploying and maintaining complex data solutions on Microsoft Azure

• Experience working in cross-functional teams