Senior Data Engineer
The Joblogic Story
Established in 1998, Joblogic is the UK’s #1 Field Service Management (FSM) software platform. We are a global business with offices in the UK, Pakistan, and Vietnam. Since our management buy-out in 2013, we have grown from ~£500K ARR to ~£35M+ ARR and expanded our team from 11 to 500+ people.
Recently, we secured a strategic growth investment from Vista Equity Partners — a global technology investor specialising in enterprise software. This investment includes over £100 million in new primary capital and will fuel our next phase of growth by accelerating our AI-first roadmap, expanding our platform into CAFM (Computer-Aided Facilities Management) capabilities, and supporting our expansion across Europe and beyond.
With Vista’s backing, we’re transforming from a successful UK business into a global scaling SaaS rocket ship, and we’d love for you to join us on our journey to £100M ARR across international markets.
Joblogic provides software to service contractors who install and maintain the built environment. Our platform helps businesses streamline operations, improve profitability, ensure compliance, and achieve rapid growth. With over 100,000 users across industries including HVAC, plumbing, electrical maintenance, facilities management, and building fabric maintenance, we are entering a new era of intelligent automation, predictive maintenance, and data-driven decision-making for service firms.
About the role
We're scaling our data department and need a senior engineer who can do two things at once: keep the data that runs the business flowing reliably today, and architect the enterprise data warehouse that will run it tomorrow.
In the near term you'll own and harden our current Azure-based ingestion and transformation pipelines. In the medium term — your biggest mandate — you'll design and implement our enterprise data warehouse / lakehouse from the ground up and lead the platform migration to Microsoft Fabric (our most likely direction) or Databricks.
This is a hands-on and leadership role. You'll set engineering standards, mentor two data engineers, and be the technical owner of the engineering branch as the team and data volumes grow. If you've built a warehouse from a blank page and want to do it again — properly, with governance and a clean semantic foundation — this is that role.
The two mandates
What you'll own today
- Own and maintain the ingestion pipelines feeding Marketing, Sales, CSM and Finance into the data lake — consistent, auditable ingestion.
- Design, build and optimise ETL/ELT pipelines in Azure Data Factory; write and tune complex SQL and stored procedures.
- Build Python automation and REST API integrations for external and third-party data.
- Enforce data quality, validation and lineage across every pipeline.
- Partner with the Analytics team so clean, modelled data feeds Power BI on time.
- Set the engineering bar (Git, code review, CI/CD) and mentor the two engineers.
- Monitor pipeline performance and cost; tune compute and storage.
What you'll build
- Architect and implement the enterprise warehouse / lakehouse end-to-end — medallion (bronze / silver / gold), dimensional models, canonical definitions, governed semantic layer.
- Lead the migration to Microsoft Fabric — OneLake, Lakehouse & Warehouse, Data Factory, Direct Lake for Power BI (or Databricks): strategy, phased cutover, legacy decommission.
- Define modelling, naming, partitioning and indexing standards and performance SLAs.
- Put governance, security, lineage and cost controls in place so the platform scales without runaway spend.
- Own the technical roadmap and architecture direction for the engineering function.
Must-have — core skills & experience
Proven, hands-on experience designing and implementing a data warehouse / lakehouse from scratch — not just maintaining one. You should be able to point to a warehouse you architected: the modelling decisions, the trade-offs, and the outcome.
- 6+ years in data engineering, including senior ownership of architecture and delivery on at least one end-to-end warehouse/lakehouse build.
- Python — data engineering, automation and API work.
- SQL — complex queries, performance optimisation and stored procedures.
- Azure Data Services:
- Azure Data Factory (ADF)
- Azure SQL Database
- Azure Storage (Blob / Data Lake)
- Azure Functions
- Data engineering fundamentals:
- ETL/ELT pipeline development
- Data modelling (incl. dimensional / star-schema)
- Data transformation
- Data validation & quality
- Database design & management
- REST API integration
- Git / version control
- Performance optimisation & scalability
- Strong problem-solving & debugging
Target platform — Microsoft Fabric
We are moving to Fabric, so this weighs heavily
- Hands-on experience with Microsoft Fabric — OneLake, Lakehouse & Warehouse, Data Factory (Fabric pipelines/dataflows) and Direct Lake semantic models for Power BI.
- Candidates who have led a migration to Fabric or Databricks are ideal.
- If you haven't used Fabric yet but have deep lakehouse experience on Databricks / Synapse and can pick it up fast — still apply.
Good to have
- Apache Spark / Databricks
- Docker & containerisation
- CI/CD pipelines
- Azure DevOps
- Power BI — semantic models
- FastAPI / Flask
- Event-driven architectures
- Service Bus· Kafka
- RabbitMQMonitoring & logging
- Data governance & security (Purview)
- Cloud cost optimisation
- Unit & integration testingAgile / Scrum
Leadership & ways of working
- Technical leadership of the engineering function — mentoring, code review, and raising the standard of the two engineers on the team.
- A clear communicator who can translate between business stakeholders, the analytics team, and engineering.
- Comfortable owning ambiguity: you can set standards and architecture from a blank page and bring others along.
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