Europe, the UK and the US. ABOUT THE ROLE Sand Technologies focuses on cutting-edge cloud-based data projects, leveraging tools such as Databricks, DBT, Docker, Python, SQL, and PySpark to name a few. We work across a variety of data architectures such as Data Mesh, lakehouse, data vault and data warehouses. Our data engineers create pipelines that support More ❯
Europe, the UK and the US. ABOUT THE ROLE Sand Technologies focuses on cutting-edge cloud-based data projects, leveraging tools such as Databricks, DBT, Docker, Python, SQL, and PySpark to name a few. We work across a variety of data architectures such as Data Mesh, lakehouse, data vault and data warehouses. Our data engineers create pipelines that support More ❯
data engineering and reporting. Including storage, data pipelines to ingest and transform data, and querying & reporting of analytical data. You've worked with technologies such as Python, Spark, SQL, Pyspark, PowerBI etc. You're a problem-solver, pragmatically exploring options and finding effective solutions. An understanding of how to design and build well-structured, maintainable systems. Strong communication skills More ❯
Champion clean code, data lifecycle optimisation, and software engineering best practices What We're Looking For Proven hands-on experience with Databricks platform and orchestration Strong skills in Python, PySpark, and SQL, with knowledge of distributed data systems Expertise in developing full lifecycle data pipelines across ingestion, transformation, and serving layers Experience with data lakehouse architecture, schema design, and More ❯
across the team. Skills & Experience Hands-on experience with Azure Databricks, Delta Lake, Data Factory, and Synapse. Strong understanding of Lakehouse architecture and medallion design patterns. Proficient in Python, PySpark, and SQL, with advanced query optimisation skills. Proven experience building scalable ETL pipelines and managing data transformations. Familiarity with data quality frameworks and monitoring tools. Experience working with Git More ❯
exposure to Natural Gas and Power markets, balancing mechanisms, and regulatory frameworks (e.g., REMIT, EMIR). Expert in Python and SQL; strong experience with data engineering libraries (e.g., Pandas, PySpark, Dask). Deep knowledge of ETL/ELT frameworks and orchestration tools (e.g., Airflow, Azure Data Factory, Dagster). Proficient in cloud platforms (preferably Azure) and services such as More ❯
SSIS, or custom scripts. Skilled in developing reusable ETL frameworks for data processing. Proficient in at least one programming language commonly used for data manipulation and scripting, including Python, PySpark, Java, or Scala. Strong understanding and hands-on experience with DevOps practices and tools, especially Azure DevOps for CI/CD, Git for version control, and Infrastructure as Code. More ❯
Required Essential Skills & Experience: 10+ years of experience in data engineering, with at least 3+ years of hands-on experience with Azure Databricks. Strong proficiency in Python and Spark (PySpark) or Scala. Deep understanding of data warehousing principles, data modelling techniques, and data integration patterns. Extensive experience with Azure data services, including Azure Data Factory, Azure Blob Storage, and More ❯
or senior technical role. Proven experience in energy trading environments, particularly Natural Gas and Power markets. Expert in Python and SQL; strong experience with data engineering libraries (e.g., Pandas, PySpark, Dask). Deep knowledge of ETL/ELT frameworks and orchestration tools (e.g., Airflow, Azure Data Factory, Dagster). Proficient in cloud platforms (preferably Azure) and services such as More ❯
business requirements into data solutions Monitor and improve pipeline performance and reliability Maintain documentation of systems, workflows, and configs Tech environment Python, SQL/PLSQL (MS SQL + Oracle), PySpark Apache Airflow (MWAA), AWS Glue, Athena AWS services (CDK, S3, data lake architectures) Git, JIRA You should apply if you have: Strong Python and SQL skills Proven experience designing More ❯
Collaborate with cross-functional teams to translate business needs into technical solutions. Core Skills Cloud & Platforms : Azure, AWS, SAP Data Engineering : ELT, Data Modeling, Integration, Processing Tech Stack : Databricks (PySpark, Unity Catalog, DLT, Streaming), ADF, SQL, Python, Qlik DevOps : GitHub Actions, Azure DevOps, CI/CD pipelines Please click here to find out more about our Key Information Documents. More ❯
Collaborate with cross-functional teams to translate business needs into technical solutions. Core Skills Cloud & Platforms : Azure, AWS, SAP Data Engineering : ELT, Data Modeling, Integration, Processing Tech Stack : Databricks (PySpark, Unity Catalog, DLT, Streaming), ADF, SQL, Python, Qlik DevOps : GitHub Actions, Azure DevOps, CI/CD pipelines Please click here to find out more about our Key Information Documents. More ❯
Collaborate with cross-functional teams to translate business needs into technical solutions. Core Skills Cloud & Platforms : Azure, AWS, SAP Data Engineering : ELT, Data Modeling, Integration, Processing Tech Stack : Databricks (PySpark, Unity Catalog, DLT, Streaming), ADF, SQL, Python, Qlik DevOps : GitHub Actions, Azure DevOps, CI/CD pipelines Please click here to find out more about our Key Information Documents. More ❯
related field with over 15 years of experience. Strong background in System Integration, Application Development, or Data-Warehouse projects across enterprise technologies. Experience with Object-oriented languages (e.g., Python, PySpark) and frameworks. Expertise in relational and dimensional modeling, including big data technologies. Proficiency in Microsoft Azure components like Azure Data Factory, Data Lake, SQL, DataBricks, HD Insights, ML Service. More ❯
experience with Azure services such as Data Factory, Databricks, Synapse (DWH), Azure Functions, and other data analytics tools, including streaming. Experience with Airflow and Kubernetes. Programming skills in Python (PySpark) and scripting languages like Bash. Knowledge of Git, CI/CD operations, and Docker. Basic PowerBI knowledge is a plus. Experience deploying cloud infrastructure is desirable. Understanding of Infrastructure More ❯
like Retrieval-Augmented Generation (RAG) and natural language analytics. What we are looking for in our candidate Essential Proficiency in Python and SQL, with experience in frameworks like Pandas, PySpark, and NumPy for large-scale data processing. Expertise in debugging and optimising distributed systems with a focus on scalability and reliability. Proven ability to design and implement scalable, fault More ❯
The ideal Data Products Engineer will have: Few years of professional software engineering experience, ideally in B2B or data product environments. Deep experience with Python, including libraries like Polars, PySpark, and frameworks such as FastAPI or Fastify and up-to-date with modern Python best practices, including tools such as Ruff, UV and PyEnv. Experience working on data-heavy More ❯
implementing data processing environments and integrations using AWS PaaS such as Glue, EMR, Sagemaker, Redshift, Aurora and Snowflake Building data processing and analytics pipelines as code, using python, SQL, PySpark, spark, CloudFormation, lambda, step functions, Apache Airflow Monitoring and reporting on the data platform performance, usage and security Designing and applying security and access control architectures to secure sensitive More ❯
AWS Data Engineer London, UK Permanent Strong experience in Python, PySpark, AWS S3, AWS Glue, Databricks, Amazon Redshift, DynamoDB, CI/CD and Terraform. Total 7 + years of experience in Data engineering is required. Design, develop, and optimize ETL pipelines using AWS Glue, Amazon EMR and Kinesis for real-time and batch data processing. Implement data transformation, streaming More ❯
development) Strong experience with CI/CD tools and pipelines for data science Solid understanding of AWS services (e.g. EC2, S3, Lambda, Glue) and CDK Proficient in Python and PySpark; SQL fluency Experience with MLflow or other model lifecycle tools Effective communicator and trainer - able to help others upskill Comfortable building internal tools and documentation Nice to Have: Experience More ❯
processes and provide training on data tools and workflows. Skills and experience • Experience in building ELT/ETL pipelines and managing data workflows. • Proficiency in programming languages such as PySPark, Python, SQL, or Scala. • Solid understanding of data modelling and relational database concepts. • Knowledge of GDPR and UK data protection regulations. Preferred Skills: • Experience with Power BI for data More ❯
Whetstone, Greater London, UK Hybrid / WFH Options
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Experience with Agile/Scrum Framework. Excellent problem-solving and analytical skills. Excellent communication skills, both at a deep technical level and stakeholder level. Data Expert experience with Databricks (PySpark). Experience building and maintaining complex ETL Projects, end-to-end (ingestion, processing, storage). Expert knowledge and experience with data modelling, data access, and data storage techniques. Experience More ❯
you. Key Responsibilities: - Design and build high-scale systems and services to support data infrastructure and production systems. - Develop and maintain data processing pipelines using technologies such as Airflow, PySpark and Databricks. - Implement dockerized high-performance microservices and manage their deployment. - Monitor and debug backend systems and data pipelines to identify and resolve bottlenecks and failures. - Work collaboratively with More ❯
