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