Enterprise Data Warehouse Engineer

The Enterprise Data Warehouse Engineer designs, builds, and operationalizes the data warehouse infrastructure and pipelines that deliver trusted, performant, and governed data for analytics and reporting. Deployed under BlueAngle’s managed services model, this engineer serves as the primary technical authority for the data platform on client engagements, working alongside client stakeholders and BlueAngle delivery leadership to ensure data is available, reliable, and fit for downstream consumption., The EDW Engineer is responsible for the full data platform stack - from source system analysis and data modelling through to pipeline development, data quality controls, and performance optimisation. The engineer works within the client environment under the governance framework defined in the BlueAngle Statement of Work, delivering against agreed milestones while maintaining professional standards of documentation and knowledge transfer., 3.1 Data Architecture & Modelling

• Lead source system analysis and data profiling across all in-scope systems.
• Design the target data model - dimensional (star/snowflake), data vault, or medallion architecture - appropriate to client’s requirements and platform.
• Define schema standards, naming conventions, and conformed dimension frameworks for downstream BI and analytics consumers.
• Produce logical and physical data model documentation as a formal deliverable (D2).

3.2 Pipeline Development & Orchestration

• Build, test, and schedule ingestion pipelines from all identified source systems (D3).
• Develop the curated/conformed transformation layer per the agreed architecture (D4).
• Implement orchestration using [dbt / Azure Data Factory / Airflow / SSIS / other] - to be confirmed with client environment.
• Apply CI/CD practices for pipeline versioning, testing, and deployment where the environment supports it.

3.3 Data Quality & Governance

• Design and implement data quality rules, validation checks, and reconciliation logic (D5).
• Build exception handling and alerting to surface data quality failures to the operations team.
• Establish data lineage documentation and maintain it throughout the engagement.
• Implement governance controls per policy defined by [Client] - the engineer implements; policy ownership remains with the client.

3.4 Performance, Optimization & Cost

• Profile and tune queries, partitioning strategies, and indexing for performance against agreed SLAs.
• Monitor platform costs and implement optimisations to stay within agreed budget targets.
• Maintain platform health, patching, and version management in line with client change management processes.

3.5 Stakeholder Engagement & Knowledge Transfer

• Produce a source and requirements assessment (D1) in collaboration with client SMEs and BlueAngle delivery leadership.
• Deliver technical documentation, runbooks, and lineage maps (D6) as formal handover artefacts at engagement close.
• Conduct structured knowledge-transfer sessions with client technical staff and partner teams.
• Participate in weekly status reporting, milestone gate reviews, and escalation processes per engagement governance.

• 8-12 years data engineering experience with at least 4 years on the Azure data stack (Synapse, Data Factory, SQL, Python or Scala).
• Strong SQL and data modelling - dimensional modelling, normalization, and/or data vault.
• Familiarity with medallion / lakehouse architecture where relevant to the target platform.
• Direct experience modelling ERP data (D365 F&O, AX, or SAP).
• At least one full-lifecycle ERP implementation where they led the warehouse / reporting layer.
• Performance tuning, query optimization, partitioning, clustering, and cost management on the target platform.
• Understanding of cloud storage patterns (data lake, blob/object storage) and their interaction with the warehouse layer.
• Experience building data quality frameworks - validation rules, reconciliation, exception reporting.
• Ability to translate technical concepts for non-technical client stakeholders.
• Strong written English - formal documentation, runbooks, and client-facing deliverables will be produced in English.
• Experience working in MSP, consulting, or client-embedded delivery contexts is strongly preferred.

1. PREFERRED CERTIFICATIONS * Cloud data platform certification - e.g. Microsoft DP-203 (Azure Data Engineer Associate), Snowflake SnowPro Core, AWS Data Analytics Specialty, or GCP Professional Data Engineer. * dbt Fundamentals certification or equivalent. * Microsoft Certified: Azure Solutions Architect Expert (advantageous for Fabric/Synapse engagements). * ITIL 4 Foundation (desirable for MSP-context engagements)., * Enterprise Data Warehouse: 8 years (Required)

Pulled from the full job description

• Professional development assistance
• 401(k)
• Health insurance
• Paid time off
• Vision insurance
• Dental insurance, * 401(k)
• Dental insurance
• Health insurance
• Paid time off
• Professional development assistance
• Vision insurance