Big Data Lead (07/05/2025)

Hirewand

London

11 months ago

Applications closed

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Job Type: Contract Job Location: Wimbledon , UK JobDescription: For this role, senior experience of Data Engineeringand building automated data pipelines on IBM Datastage & DB2,AWS and Databricks from source to operational databases through tocuration layer is expected using the latest cloud moderntechnologies where experience of delivering complex pipelines willbe significantly valuable to how to maintain and deliver worldclass data pipelines. Knowledge in the following areas essential: -Databricks: Expertise in managing and scaling Databricksenvironments for ETL, data science, and analytics use cases. - AWSCloud: Extensive experience with AWS services such as S3, Glue,Lambda, RDS, and IAM. - IBM Skills: DB2, Datastage, Tivoli WorkloadScheduler, Urban Code - Programming Languages: Proficiency inPython, SQL. - Data Warehousing & ETL: Experience with modernETL frameworks and data warehousing techniques. - DevOps &CI/CD: Familiarity with DevOps practices for data engineering,including infrastructure-as-code (e.g., Terraform, CloudFormation),CI/CD pipelines, and monitoring (e.g., CloudWatch, Datadog). -Familiarity with big data technologies like Apache Spark, Hadoop,or similar. - ETL/ELT tools and creating common data sets acrosson-prem (IBMDatastage ETL) and cloud data stores - Leadership &Strategy: Lead Data Engineering team(s) in designing, developing,and maintaining highly scalable and performant datainfrastructures. - Customer Data Platform Development: Architectand manage our data platforms using IBM (legacy platform) &Databricks on AWS technologies (e.g., S3, Lambda, Glacier, Glue,EventBridge, RDS) to support real-time and batch data processingneeds. - Data Governance & Best Practices: Implement bestpractices for data governance, security, and data quality acrossour data platform. Ensure data is well-documented, accessible, andmeets compliance standards. - Pipeline Automation &Optimisation: Drive the automation of data pipelines and workflowsto improve efficiency and reliability. - Team Management: Mentorand grow a team of data engineers, ensuring alignment with businessgoals, delivery timelines, and technical standards. - Cross CompanyCollaboration: Work closely with all levels of business stakeholderincluding data scientists, finance analysts, MI andcross-functional teams to ensure seamless data access andintegration with various tools and systems. - Cloud Management:Lead efforts to integrate and scale cloud data services on AWS,optimising costs and ensuring the resilience of the platform. -Performance Monitoring:Establish monitoring and alerting solutionsto ensure the high performance and availability of data pipelinesand systems to ensure no impact to downstream consumers.#J-18808-Ljbffr

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