12 of 12 Apache Spark Jobs in the City of London

code for data workloads (Terraform or Bicep) * Databricks - Delta Lake, Unity Catalog, job cluster vs interactive cluster trade-offs, cost-aware compute management, Spark job optimisation * Relational databases: PostgreSQL at production scale - query optimisation, indexing strategies, table partitioning, replication, schema design for both OLTP and analytical workloads * MongoDB - document … documented data products * Nice to Have * Financial markets data - market data feeds (Bloomberg, Refinitiv), tick data, trade history, reference data, or instrument master management * Apache Spark or Flink for large-scale stream and batch processing beyond the Databricks ecosystem * dbt or equivalent SQL transformation layer; experience building ...

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 ...

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 ...

What the role entails: Some of the main duties of the Data Engineer will include: Develop high-performance data pipelines using Python (Pandas) or Spark within Palantir Foundry, ensuring seamless data transformation and integration to support analytics, reporting, and machine learning use cases across the enterprise. Design and implement ...

support Workflow orchestration (Airflow or Dagster) AWS data services (Athena, S3, MWAA, Iceberg) Data quality monitoring & incident management Nice to Have: Airbyte DBT Apache Spark Grafana FTP/data distribution platforms Financial data experience ...

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 ...

will have strong experience in data engineering, with advanced proficiency in SQL and Python, alongside exposure to large-scale data processing frameworks such as Apache Spark. You should have a solid understanding of data architecture, data wrangling and data quality practices, as well as experience working within financial markets ...

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 ...

influence within the team and communicate clearly with technical and non-technical stakeholders. Data Engineer (New Technology/Microsoft) Strong experience with Databricks (Spark, PySpark, Delta Lake, and Unity Catalog advantageous). Proficiency in Azure data services (Azure Data Factory, Data Lake, Azure Functions advantageous). Experience contributing … Engineer or Solutions Architect certification (desirable). Databricks Certified Data Engineer or Machine Learning Associate (desirable). Experience with streaming solutions (Kafka, Event Hubs, Spark Streaming) (desirable). Knowledge of machine learning and AI on Databricks (desirable). Benefits of working at Canada Life We believe in recognising ...

with Git, Python, and Ansible Familiarity with containers (Docker, Kubernetes, LXC) Experience with monitoring tools and SQL databases Exposure to big data systems (e.g., Spark, HDFS, Airflow) Summary A great opportunity for someone who enjoys solving complex infrastructure challenges, automating systems, and working closely with engineering teams. Spectrum ...

deploy scalable, secure data solutions using Azure Databricks, Data Factory and Data Lake Storage. Develop and optimise advanced data pipelines with Python, SQL, Spark/PySpark and Delta Lake. Champion strong data quality, governance and observability practices. Modernise legacy systems and support large-scale migrations into Azure. Provide technical … Minimum Criteria Extensive experience with Azure services including Azure Databricks, Azure Data Lake Storage, and Azure Data Factory. Advanced proficiency in SQL, Python, and Spark (PySpark), with a strong focus on performance optimization and distributed processing. Proven experience in CI/CD practices using industry-standard tools (e.g., GitHub ...

learning or LLM-based systems Exposure to enterprise data platforms or similar tools Frontend development experience (e.g., React) Familiarity with distributed computing frameworks (e.g., Spark) What’s on Offer Competitive compensation package, including equity Comprehensive benefits Hybrid working model Opportunity to build products and systems from the ground ...