Job Specifications
Our client is is seeking an experienced Lead Data Architect / Engineering Consultant to deliver the cloud-native data platform. This platform will serve as the backbone for ingesting, analysing, and visualising operational data - enabling real-time monitoring, alerting, and long-term trend analysis. You will work across disciplines (engineering, analytics, asset operations, trading) to design a scalable, secure, and high-performance platform that supports current needs and enables future growth.
The key responsibilities are the following:
Project Ownership:
Lead the end-to-end delivery of the data & analytics platform, from discovery through production handover.
Define the architectural vision, design decisions, and implementation roadmap; coordinate closely with internal stakeholders.
Facilitate technical discovery sessions with the data, trading, and operations teams to capture cross-functional requirements.
Ensure that documentation, codebases, and data models are structured to support maintainability and reuse beyond the project.
Architecture & Platform Design
Design a modular, multi-tenant architecture to accommodate diverse asset telemetry and operational datasets.
Select appropriate Azure-native technologies and apply scalable data patterns for ingestion, processing, storage, and access.
Incorporate Medallion architecture principles with time-series and event data optimised for both real-time and historical analytics.
Ensure interoperability with existing platforms (BI tools, trading models, performance dashboards).
Engineering & Implementation
Build robust batch and streaming pipelines using Azure Data Factory, Databricks, and Spark Structured Streaming.
Implement secure, scalable storage using Delta Lake formats, Azure Data Lake Gen2, and time-series solutions (e.g. Azure Data Explorer).
Deliver data validation, schema evolution, metadata management, and lineage tracking to production quality.
Mentoring & Capability Building
Actively mentor and upskill internal data engineers and analysts throughout the project.
Establish coding standards, design patterns, and operational best practices tailored to the internal capabilities.
Support the Analytics team in developing a roadmap for sustained platform ownership and ongoing enhancements.
Where appropriate, help integrate platform components with broader trading and forecasting infrastructure.
Governance, Security & Handover
Define and implement access control, encryption, and cost monitoring policies aligned with Azure best practices.
Produce complete documentation for infrastructure, pipelines, operational procedures, and support processes.
Provide transition and onboarding support to ensure the internal team can own and evolve the platform post-delivery.
Required Skills & Experience
Extensive experience delivering enterprise-scale data platforms in cloud environments(preferably Azure).
Deep expertise in lake house architectures, real-time/batch processing, and telemetry/IoT pipelines.
Proven ability to lead complex delivery across technical and non-technical teams.
Strong hands-on experience with:
Azure Event Hubs, IoT Hub, Data Factory, Data Lake Gen2, Synapse, Databricks
Spark, Structured Streaming, Delta Lake, Parquet
SQL, Python
Time-series DBs (Azure Data Explorer, InfluxDB, TimescaleDB)
Familiarity with Medallion architecture, schema evolution, cost optimisation, and system observability.
Track record of mentoring internal teams and embedding best practices in technical delivery.
Experience in energy, grid operations, or industrial IoT highly desirable.
FTC: 6 to 12 months. There is flexibility to extend the scope of this engagement to review and upgrade the wider data infrastructure supporting Analytics and Trading functions.
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