1 to 25 of 42 Apache Spark Jobs in London

code. Perfect if you want scope for growth without going “post-technical.” What you’ll do Design and build modern data platforms using Databricks, Apache Spark, Snowflake, and cloud-native services (AWS, Azure, or GCP). Develop robust pipelines for real-time and batch data ingestion from diverse … Lead Data Engineer in complex enterprise environments. Can demonstrate strong coding skills in Python (Scala or functional languages a plus). Expertise with Databricks, Apache Spark, and Snowflake is a must (HDFS/HBase also useful). Has experience integrating large, messy datasets into reliable, scalable data products. ...

ESSENTIAL CRITERIA: Strong experience designing and operating large-scale distributed or event-driven systems. Hands-on experience with streaming and data technologies such as Apache Kafka, Apache Flink, Spark, or similar platforms. Experience building scalable cloud-based services, ideally within AWS environments and modern backend technologies such … used across our teams include: Java/Spring Boot and TypeScript/JavaScript AWS services including MSK, Lambda, ECS, S3, DynamoDB, Redshift, and SQS Apache Kafka, Apache Flink, Airflow, Spark, and EventBridge SQL and NoSQL databases including Postgres, MongoDB, DynamoDB, and Timestream CI/CD and automation ...

Data Engineer, working with large-scale, complex datasets in regulated environments Scala development experience supporting analytics or data-processing solutions Hands-on experience with Apache Spark for large-scale distributed data processing Experience with model calibration, threshold management, and score explainability to meet AML governance and regulatory standards … Analyst or Data Engineer, working with large, complex datasets in regulated environments Solid Scala development experience, writing and supporting scalable data and analytics solutions Apache Spark experience, including batch and/or dynamic processing of high-volume data About Us FDM is an award-winning global leader ...

data engineering tasks. Experience with cloud platforms such as: AWS, Azure, Google Cloud Platform (GCP) Hands-on experience with data technologies such as: Apache Spark, Databricks, Kafka, Airflow, Snowflake, Hadoop ecosystems Experience designing and maintaining data warehouses and data lakes. Knowledge of DevOps and CI/CD tooling ...

understand where traditional patterns remain appropriate - and where modern approaches add measurable value. Data Engineering & Platform Understanding: Strong knowledge of distributed processing (e.g. Apache Spark). Familiarity with Hadoop ecosystem components. Working knowledge of SQL and NoSQL technologies. Understanding of ETL/ELT design trade-offs. Awareness ...

understand where traditional patterns remain appropriate - and where modern approaches add measurable value. Data Engineering & Platform Understanding: Strong knowledge of distributed processing (e.g. Apache Spark). Familiarity with Hadoop ecosystem components. Working knowledge of SQL and NoSQL technologies. Understanding of ETL/ELT design trade-offs. Awareness ...

Collaborate with senior developers and quantitative teams on new analytics capabilities Participate in debugging, testing, and production support activities Technology Environment Python Databricks/Apache Spark SQL Pandas/NumPy Git REST APIs Cloud technologies (Azure or AWS) Numerix (or equivalent pricing/risk platform) Jupyter/notebook … commercial Python development experience Strong understanding of data structures and software engineering fundamentals Experience working with data engineering or analytics tooling Exposure to Databricks, Spark, or cloud-based data platforms Understanding of SQL and relational databases Excellent problem-solving and communication skills STEM or Computer Science degree preferred Nice ...

focus on production-quality code • Solid understanding of ETL processes and best practices • Experience with SQL/NoSQL databases and big data tools (e.g. Spark, Pulsar) • Hands-on experience with cloud platforms (AWS, Azure, or GCP) • Experience developing and deploying data services end-to-end • Strong stakeholder communication ...

focus on production-quality code • Solid understanding of ETL processes and best practices • Experience with SQL/NoSQL databases and big data tools (e.g. Spark, Pulsar) • Hands-on experience with cloud platforms (AWS, Azure, or GCP) • Experience developing and deploying data services end-to-end • Strong stakeholder communication ...

focus on production-quality code • Solid understanding of ETL processes and best practices • Experience with SQL/NoSQL databases and big data tools (e.g. Spark, Pulsar) • Hands-on experience with cloud platforms (AWS, Azure, or GCP) • Experience developing and deploying data services end-to-end • Strong stakeholder communication ...

capacity planning, skills development, and vendor contributions The individual: Expertise in Azure data services (ADLS, ADF, Databricks, Synapse, Event Hub/Kafka), Python, SQL, Spark/PySpark. Experience with data modeling, metadata and lineage tooling; performance & cost optimization. Proven leadership in engineering teams; Agile/DevOps ways of working ...

capacity planning, skills development, and vendor contributions The individual: Expertise in Azure data services (ADLS, ADF, Databricks, Synapse, Event Hub/Kafka), Python, SQL, Spark/PySpark. Experience with data modeling, metadata and lineage tooling; performance & cost optimization. Proven leadership in engineering teams; Agile/DevOps ways of working ...

commodities markets or broader financial markets. Knowledge of quantitative modeling, risk management, or algorithmic trading. Familiarity with big data technologies like Kafka, Hadoop, Spark, or similar. Why Work With Us? Impactful Work: Directly influence the profitability of the business by building technology that drives trading decisions. Innovative Culture ...

auditable, lineage-aware, idempotent, and resilient in regulated environments. Implement robust data quality controls including testing, validation, monitoring, and alerting. Drive performance optimization across Spark and Snowflake workloads. Partner with Product, Engineering, DevOps, and Regulatory teams to translate requirements into scalable technical designs. Contribute to the continuous improvement ...

warehousing concepts Familiarity with version control systems, particularly Git Desirable Skills: Experience with infrastructure as code tools such as Terraform or CloudFormation Exposure to Apache Spark for distributed data processing Familiarity with workflow orchestration tools such as Airflow or AWS Step Functions Understanding of containerisation using Docker Experience ...

cloud platforms (GCP or Azure) and Databricks. Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML. Nice-to-have: Experience with Spark, CI/CD pipelines, and orchestration tools. Knowledge of Elasticsearch or digital/web analytics platforms. Understanding of the full machine learning lifecycle, from ...

looking for Strong Python + SQL (essential) 5+ years in data engineering/platform engineering AWS experience in production data environments Hands-on with Spark/Kafka/Snowflake/Databricks (or similar) Experience with data lakes/lakehouse/distributed systems Background in financial services, trading, or quantitative ...

Proficient SQL skills for data analysis, validation, and troubleshooting. Experience working with data warehouses, data lakes, or big data platforms (e.g., Snowflake, Redshift, BigQuery, Spark). Engineering Practices & Tooling Experience with CI/CD pipelines and automated build and deployment processes. Strong experience with version control systems (Git). ...

Proficient SQL skills for data analysis, validation, and troubleshooting. Experience working with data warehouses, data lakes, or big data platforms (e.g., Snowflake, Redshift, BigQuery, Spark). Engineering Practices & Tooling Experience with CI/CD pipelines and automated build and deployment processes. Strong experience with version control systems (Git). ...

bring: Strong SQL + ETL/ELT experience Knowledge of Python/T-SQL Exposure to cloud platforms (Azure preferred) Nice to have: Databricks, Spark, CI/CD, big data This is a great opportunity to join a high-impact data team in a global organisation and shape ...

Azure Key Vault, Azure Monitor, Azure Log Analytics, Azure API Management (APIM), Azure DevOps. Databases : SQL Server, Oracle, PostgreSQL, Delta Lake Big Data : Apache Spark Version Control : GitHub, Git, Azure DevOps Visualization : Power BI & Integration with REST APIs for custom dashboards. Data Integration & Workflow Orchestration : Azure Data Factory ...

Docker and Kubernetes is a plus. Experience designing end to end ML data pipelines. Experience with database technologies such as PostgreSQL, MySQL, or MongoDB, Spark, Databricks and stream data processing such as Kafka, RedPanda is a plus. Direct experience in building and applying large-scale (100M+ users) machine learning ...

experience in software engineering, MLOps, and deploying machine learning models at scale.* Proficiency in SQL, Python, and familiarity with big data technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure).* Industry knowledge: Experience in gaming or digital entertainment is a strong plus. ...

experience in software engineering, MLOps, and deploying machine learning models at scale. Proficiency in SQL, Python, and familiarity with big data technologies (e.g., Kafka, Spark) and/or cloud platforms (e.g., GCP, AWS, or Azure). Industry knowledge: Experience in gaming or digital entertainment is a strong plus. ...

robust, scalable, and governed data solutions using modern cloud technologies. The Role Own the end-to-end design, build, optimisation, and support of scalable Spark/PySpark data pipelines (batch and streaming) Define and implement lakehouse architecture standards (medallion model: bronze, silver, gold), including governance, lineage, and data quality … controls Design and manage secure data ingestion frameworks (e.g. Apache NiFi, APIs, SFTP/FTPS) for internal and external data sources Architect and maintain secure AWS-based data infrastructure (S3, IAM, KMS, Glue, Lake Formation, Lambda, Step Functions, CloudWatch, etc.) Implement orchestration using tools such as Airflow, Databricks Workflows ...