Senior Data Engineer

Sand Technologies is a global Physical AI company using data and AI to make critical industries work better. We partner with governments, cities and enterprises to improve how essential systems operate across healthcare, water, energy, telecommunications and infrastructure.

Our work delivers proven real-world impact. We have built AI systems that help manage London’s water supply, supported telecom network planning across hundreds of cities, and developed digital healthcare platforms serving tens of millions of people across Africa. From intelligent command centers to AI-powered infrastructure platforms, we help organizations sense, analyze and act in complex environments.

Our people are ambitious, curious and relentlessly practical. Our teams work alongside clients in the field, solving hard problems and deploying solutions that last. With colleagues across Africa, Europe, the UK and the US, we operate across the full stack - from research and engineering to deployment and capability building.

Our mission is simple: to harness AI to solve humanity’s most pressing challenges.

We are looking for a Senior Data Engineer to join our UK utilities practice as the technical backbone of the data platform capability. This is a permanent role. Your first assignment is building an AI-enabled situational awareness and decision-support platform on Microsoft Azure for a major UK water utility, and from there you will continue delivering across our growing portfolio of water and energy utility clients.

Utility data landscapes are heterogeneous - operational and asset systems spanning SCADA telemetry, electronic logbooks, ERP, incident-reporting and third-party predictive platforms, connected via a mix of APIs, file transfer and manual re-entry, with streaming (MQTT) as the future state. Your mission is to establish the reusable ingestion and modelling patterns the rest of the data engineering squad follows, and to define the canonical data models that feed the analytical risk indices and the intelligence layer.

Our clients operate critical national infrastructure, so reliability, security and data governance are paramount. You won’t just write pipelines – you will set the patterns, work directly with client data owners and enterprise architects to land integrations, and mentor the mid-level and junior engineers who build alongside you.

• Ingestion & Pipeline Build: Design and build robust, reusable data ingestion pipelines from clients’ operational estates into the Azure data platform, covering API, SFTP/file, batch and (future-state) streaming/MQTT patterns.
• Data Architecture & Modelling: Define the canonical data models and transformation logic that feed the analytical indices and intelligence layer; design schemas for high-performance storage, retrieval and analysis using lakehouse patterns.
• Patterns & Standards: Establish and document ingestion and modelling patterns so mid-level and junior engineers can replicate them consistently as the platform scales.
• Client Collaboration: Partner with client data owners, data stewards and enterprise architecture teams to agree access, security and integration approaches per source system.
• Quality, Governance & Operations: Own data quality, lineage, observability and pipeline reliability across the platform; implement governance and security measures appropriate to a regulated, critical-infrastructure environment.
• Leadership & Mentoring: Mentor and unblock the mid-level and junior data engineers, review their work, and promote engineering best practice across the squad.

• Proven experience as a Senior Data Engineer, with hands-on experience building and optimising production data pipelines and designing data architectures.
• Strong commercial experience on the Azure data platform (e.g. Data Factory, Synapse, Fabric, ADLS, Databricks or equivalent).
• Expert SQL and Python; solid data modelling across dimensional and/or lakehouse patterns.
• Proven experience integrating messy, heterogeneous source data - transferred via API, file transfer, batch or streaming - into coherent, well-modelled datasets.
• Experience setting standards and patterns, and leading or mentoring other engineers in delivery.
• Comfortable working directly with client data owners, stewards and architects to land integrations, and able to communicate technical concepts to non-technical stakeholders.
• Knowledge of data governance, quality frameworks and security practices in regulated environments.

• Exposure to operational technology (OT)/SCADA, IoT/asset telemetry or MQTT systems.
• Utilities, water or other asset-intensive industry experience.
• Streaming and big-data tooling (Kafka, Spark/Spark Streaming, Flink).
• Understanding of machine learning workflows and how to support them with robust data pipelines.

Due to the highly collaborative and internationally distributed nature of our work, successful candidates must be comfortable operating in small teams while contributing to larger, globally coordinated efforts. A strong sense of ownership, self-motivation and discipline in maintaining clear and consistent communication through virtual collaboration tools and video conferencing is essential.