Job Description

Senior Data Engineer

Powering the future requires serious data infrastructure. We’re looking for a Senior Data Engineer to design, scale, and champion modern data solutions for us

About Sand

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.

About the role

As a Senior Data Engineer, your primary mission is to design, build, and maintain scalable data pipelines and robust infrastructure to support data-intensive applications and advanced analytics solutions. Working with our UK-based Utilities client, you will handle critical infrastructure data, meaning reliability, scalability, and security are paramount.

In this role, you won't just write code, you will architect modern data solutions, oversee key engineering projects, and mentor growing engineering talent. You will collaborate closely with cross-functional teams to shape the strategic direction of our data initiatives.

What you’ll do

• Data Pipeline & ETL Development: Lead the design, implementation, and automation of scalable ETL/ELT workflows. Ingest, process, and transform large volumes of structured and unstructured data from diverse sources (including IoT and asset telemetry) into data lakes or lakehouses.
• Modern Data Architecture & Modeling: Architect efficient, modern data solutions (e.g., Lakehouse architectures). Design and optimize data models and schemas for high-performance storage, retrieval, and analysis.
• Big Data & Cloud Engineering: Leverage big data technologies (Spark, Kafka, Flink) and cloud-native services (AWS, Azure, or GCP) for distributed data processing and real-time streaming applications.
• Operations, DevOps & CI/CD: Build and maintain CI/CD pipelines, manage version control, and deploy containerized solutions. Monitor infrastructure performance, identify bottlenecks, and optimize for cost-efficiency and reliability.
• Data Quality & Governance: Implement and oversee robust data governance, quality frameworks, and security measures ensuring compliance with industry standards.
• Collaboration & Documentation: Partner with data scientists, analysts, and business stakeholders to translate requirements into technical reality. Create clear technical documentation, including data architecture diagrams and workflow maps.
• Leadership & Innovation: Evaluate and recommend emerging technologies and frameworks. Mentor and guide junior and mid-level data engineers, promoting engineering best practices.

Who you are

• Proven experience as a Senior Data Engineer, or in a similar role, with hands-on experience building and optimizing data pipelines and infrastructure, and designing data architectures.
• Proven experience working with Big Data and tools used to process Big Data
• Strong problem-solving and analytical skills with the ability to diagnose and resolve complex data-related issues.
• Excellent understanding of data engineering principles and practices.
• Excellent communication and collaboration skills to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.
• Ability to adapt to new technologies, tools, and methodologies in a dynamic and fast-paced environment.
• Ability to write clean, scalable, robust code using python or similar programming languages. Background in software engineering is a plus.
• Knowledge of data governance frameworks and practices.
• Understanding of machine learning workflows and how to support them with robust data pipelines.

Desirable languages/tools

• Core Languages: Python, SQL, Scala, or Java (for data manipulation and scripting).
• Data Processing & Streaming: Apache Spark, Databricks, Kafka, Flink, or Spark Streaming.
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and their native data tools (e.g., S3/Blob, EMR, Redshift, Synapse, Glue, ADF, BigQuery, Dataflow).
• Data Orchestration & Warehousing: Apache Airflow, modern Lakehouse patterns, or enterprise ETL tools (Informatica, Talend).
• DevOps Tools: Git, CI/CD pipelines, and containerization tools (Docker/Kubernetes).
• Methodologies: Advanced relational and dimensional data modeling techniques.

How we work

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.