Data Engineer
Skills
About This Role
Overview
- The Data Engineer will play a key role in the Digital Transformation.
- You'll initially work alongside our implementation partner during the build of the new data lake, then take full ownership of run-and-maintain and project delivery as the platform evolves.
- The role requires hands-on data architecture and delivery experience, alongside the ability to support business growth through technology and innovation.
Data Lake Build & Pipeline Development
- Work alongside the implementation partner to design and build the data lake - ingestion, storage zones (raw / curated / consumption), and access patterns.
- Work closely with internal stakeholders (Finance, Operations, IT, Commercial Business Leads) during the development phase.
- Build and test data pipelines (batch and near-real-time) using Apache Spark on Azure, sourcing from the ERP, legacy databases, SharePoint, and other operational systems (e.g CRM).
- Translate business requirements into data models, and pipeline designs; critically evaluate the partner's designs.
- Ensure pipelines are repeatable, monitored, and recoverable, with clear logging and lineage.
Data Architecture & Modelling
- Maintain and evolve the data architecture as new domains come on board (Finance, Sales, Parts, Service, HR)
- Apply data architecture patterns appropriate to each use case.
- Propose and implement a plan to upload historical data to maintain report history.
- Define and document data models, KPI definitions, and metric contracts so report consumers can rely on the numbers.
- Structure and shape consumption-layer datasets so they are directly fit for Power BI dashboards and reports - including star/snowflake modelling, conformed dimensions, semantic-friendly naming, and the right granularity and aggregations to keep reports performant. Partner with report developers and business users to translate analytical requirements into reusable, well-governed datasets.
- Own technical documentation - architecture diagrams, data lineage, runbooks - to ensure continuity beyond the implementation phase.
Data Quality, Governance & Audit
- Embed data quality checks, cataloguing, and lineage tooling (Microsoft Purview or equivalent) so the lake remains trustworthy as it grows.
- Identify, document, and resolve data discrepancies, gaps, and integrity issues across source and target systems.
- Support data audit, compliance, and access-governance requirements across the platform.
- Maintain auditable logs of pipeline runs, integration outcomes, and data quality results.
Platform Operations & BAU Ownership
- Take operational ownership of the data lake post go-live: monitoring, incident response, performance tuning, cost control, and capacity planning.
- Extend the platform with new sources and use cases as the business evolves.
- Define and meet platform service levels, including availability, data freshness, and issue resolution times.
- Continuously improve platform standards, documentation, and engineering practices based on operational feedback.
System & Cloud Integration
- Design and deliver integrations between the data lake and source systems - REST APIs, webhooks, SFTP, and SaaS connectors.
- Operationalise integrations across Microsoft Azure, Google Workspace, and third-party cloud platforms used by the business.
- Maintain integration security, authentication, and credential management aligned with Group IT standards.
- Diagnose and resolve integration failures with structured root-cause analysis.
Technical & Analytical Skills
- Strong working knowledge of Microsoft Azure data services - Azure Data Lake Storage, Azure Data Factory, Synapse / Fabric, Azure SQL, etc
- Solid Apache Spark experience (PySpark or Scala), including performance tuning, partitioning, and Delta Lake or equivalent table formats.
- Working knowledge of all core data lake components - ingestion frameworks, orchestration, schema evolution, file formats, and governance layers.
- Practical experience with Google Workspace as a data source and admin environment, plus Google Cloud familiarity (BigQuery, GCS) at a working level
- Strong system and cloud integration experience - REST APIs, webhooks, message queues, file-based integrations, and SaaS connectors.
- Strong SQL and Python; clean, readable, version-controlled code.
- Exposure to Microsoft Purview or similar data audit / catalogue / lineage tools is advantageous.
- Exposure to Microsoft Dynamics 365 data extraction or other large ERP environments is preferred.
- Demonstrated ability to design and structure consumption-layer datasets for Power BI. Comfortable working hand-in-hand with report developers and business users to shape the data the way the dashboards need it.
Qualifications & Experience
- Bachelor's degree in a relevant field (Computer Science, Information Systems, Data Engineering, Software Engineering, Mathematics, or equivalent practical experience).
- 5-7 years' experience as a Data Engineer, with at least one end-to-end data lake or lakehouse build delivered.
- Demonstrated experience designing and operating data pipelines on Azure, using Spark / Delta Lake at production scale.
- Experience supporting or extracting data from Microsoft Dynamics 365 or another large ERP is preferred.
- Exposure to Microsoft Purview or similar data governance / audit tools is advantageous.
- Hands-on experience structuring data for Power BI on the consumption side of a data lake or warehouse. Exposure to other BI tooling is a plus, but Power BI experience is required.
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