Senior Data Engineer (Microsoft Fabric)

Joblogic, established in 1998, is the UK’s leading Field Service Management (FSM) software platform with a global presence including offices in the UK, Pakistan, and Vietnam. Since a management buy-out in 2013, the company has experienced significant growth, expanding from approximately £500K ARR to around £35M ARR and growing its team from 11 to 500 employees. Recently, Joblogic secured a strategic growth investment exceeding £100 million from Vista Equity Partners, a global technology investor specializing in enterprise software. This investment is driving the company’s AI-first roadmap, expanding into Computer-Aided Facilities Management (CAFM), and supporting its European and international growth ambitions. Joblogic’s platform serves over 100,000 users across industries such as HVAC, plumbing, electrical maintenance, facilities management, and building fabric maintenance, enabling businesses to streamline operations, improve profitability, and embrace intelligent automation and predictive maintenance.

Key Responsibilities

- Own and maintain Azure-based data ingestion pipelines feeding Marketing, Sales, Customer Success, and Finance teams, ensuring consistent and auditable data flows.
- Design, build, and optimize ETL/ELT pipelines using Azure Data Factory, including writing and tuning complex SQL queries and stored procedures.
- Develop Python automation scripts and REST API integrations for external and third-party data sources.
- Enforce data quality, validation, and lineage across all pipelines to maintain data integrity.
- Collaborate closely with the Analytics team to provide clean, modeled data for Power BI dashboards on schedule.
- Set engineering standards including Git workflows, code reviews, and CI/CD processes while mentoring two data engineers.
- Monitor pipeline performance and costs, tuning compute and storage resources for efficiency.
- Architect and implement an enterprise data warehouse/lakehouse from the ground up, including medallion architecture (bronze, silver, gold layers), dimensional models, canonical definitions, and a governed semantic layer.
- Lead the migration strategy and phased cutover to Microsoft Fabric (or Databricks), managing legacy system decommissioning.
- Define standards for data modeling, naming conventions, partitioning, indexing, and establish performance SLAs.
- Implement governance, security, lineage, and cost controls to ensure scalable and sustainable platform growth.
- Own the technical roadmap and architectural direction for the data engineering function.

Required Qualifications

- Minimum of 6 years’ experience in data engineering with senior ownership of at least one end-to-end data warehouse or lakehouse build.
- Proven hands-on experience designing and implementing a data warehouse or lakehouse from scratch, with clear examples of architectural decisions and outcomes.
- Strong proficiency in Python for data engineering, automation, and API integration.
- Advanced SQL skills, including complex query writing, performance tuning, and stored procedures.
- Expertise with Azure Data Services, including Azure Data Factory, Azure SQL Database, Azure Storage (Blob/Data Lake), and Azure Functions.
- Solid understanding of data engineering fundamentals such as ETL/ELT pipeline development, data modeling (dimensional/star-schema), data transformation, validation, quality, and database design.
- Experience integrating REST APIs and using Git/version control systems.
- Strong problem-solving and debugging skills with a focus on performance optimization and scalability.

Preferred Qualifications and Benefits

- Hands-on experience with Microsoft Fabric components such as OneLake, Lakehouse & Warehouse, Data Factory pipelines/dataflows, and Direct Lake semantic models for Power BI is highly desirable.
- Experience leading migrations to Microsoft Fabric or Databricks platforms is a strong advantage.
- Familiarity with Apache Spark, Databricks, Docker/containerization, CI/CD pipelines, Azure DevOps, Power BI semantic models, FastAPI/Flask, event-driven architectures, messaging systems (Service Bus, Kafka, RabbitMQ), monitoring, logging, data governance (Purview), cloud cost optimization, and Agile/Scrum methodologies is beneficial.
- Proven technical leadership skills, including mentoring, code review, and raising engineering standards within a team.
- Excellent communication skills to bridge business stakeholders, analytics, and engineering teams.
- Comfortable working in ambiguous environments, capable of setting standards and architecture from scratch while guiding others.

This role offers a unique opportunity to lead and shape the data platform of a fast-growing SaaS company on a global scale, driving innovation and operational excellence in a dynamic environment.