Job Description
Make sure to read the full description below, and please apply immediately if you are confident you meet all the requirements.
About SynextraSynextra 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 RoleWe’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 xwzovoh 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
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