Data Engineer

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 Sand Technologies build data-intensive systems that enable insight, intelligence, and informed decision-making. We typically work with hybrid data architectures with centralised lakehouses or data warehouses and distributed data products on top. Our stack includes tools such as Databricks, dbt, Docker, Python, SQL, and PySpark. We primarily work in cloud-native environments across AWS, Azure, and GCP, while occasionally supporting self-hosted open-source deployments. Our Data Engineer is responsible for designing, building, and maintaining scalable data architecture that underpins our decision-support applications. Our decision-support applications range from traditional Analytics (data warehouse), to Machine Learning, to Digital Twins and on occasion serving LLMs and Agentic workflows, and as such your data architecture should support various use cases. You will work closely with cross-functional teams and contribute to the strategic direction of our data initiatives. We operate with a strong code-first, "data as a product" mindset, where testing, reliability, observability, and performance are non-negotiable. Specific Responsibilities Architect and build a secure, scalable urban data platform integrating multi‑agency and infrastructure datasets at scale. Design resilient cloud‑native architectures supporting batch, streaming, and near‑real‑time operational workloads. Lead development of high‑performance ingestion and transformation pipelines across legacy systems, APIs, IoT/telemetry, and structured data sources. Implement distributed and event‑driven processing systems (e.g., Spark, Kafka or equivalent) for large‑scale analytical and operational use cases. Establish platform reliability standards, including observability, automated data quality validation, lineage, monitoring, and defined SLAs/SLOs. Design and enforce strong data governance and access control frameworks, including RBAC, encryption, auditability, and secure data handling practices. Build modern lakehouse or equivalent architectures that enable advanced analytics, GIS, and production‑grade machine learning. Partner closely with data scientists, ML engineers, and senior stakeholders to operationalize AI and analytics at scale. Optimize platform performance, scalability, and cost efficiency as adoption grows. Contribute to long‑term architectural direction and mentor engineering team members. Requirements – Essential 3+ years designing and operating large‑scale semi‑distributed data platforms (hybrid centralised and distributed) in cloud or hybrid environments. Proven experience architecting modern data systems (lakehouse, data mesh, or equivalent) supporting both analytical (descriptive and predictive) and operational workloads. Deep hands‑on expertise with distributed processing frameworks (e.g., Spark) and streaming/event systems (e.g., Kafka or similar). Strong experience building secure, governed data environments with robust access controls, encryption, lineage, and audit capabilities. Experience designing secure data platforms in regulated or government environments, with strong understanding of compliance, auditability, and data protection standards. Experience integrating heterogeneous data sources, including legacy systems, APIs, telemetry/IoT systems, and relational databases. Demonstrated ability to design highly available, observable, production‑grade data systems. Experience enabling machine learning and advanced analytics through robust data infrastructure and feature pipelines. Strong proficiency in Python, SQL, and ideally DBT with a track record of writing clean, production‑quality code. Experience deploying and operating solutions in AWS, Azure, or GCP, including CI/CD and infrastructure‑as‑code is beneficial. Ability to operate effectively in complex, multi‑stakeholder environments. #J-18808-Ljbffr