Big Data Lead

For this role, senior experience of Data Engineering and building automated data pipelines on IBM Datastage & DB2, AWS, and Databricks from source to operational databases through to curation layer is expected using the latest cloud modern technologies. Experience delivering complex pipelines will be significantly valuable in how D&G maintains and delivers world-class data pipelines.

Knowledge in the following areas is essential:

• Databricks: Expertise in managing and scaling Databricks environments for ETL, data science, and analytics use cases.
• AWS Cloud: Extensive experience with AWS services such as S3, Glue, Lambda, RDS, and IAM.
• IBM Skills: DB2, Datastage, Tivoli Workload Scheduler, Urban Code.
• Programming Languages: Proficiency in Python and SQL.
• Data Warehousing & ETL: Experience with modern ETL 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 across on-prem (IBM DataStage ETL) and cloud data stores.
• Leadership & Strategy: Lead Data Engineering teams in designing, developing, and maintaining highly scalable and performant data infrastructures.
• Customer Data Platform Development: Architect and manage 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 processing.
• Data Governance & Best Practices: Implement best practices for data governance, security, and data quality across the platform. Ensure data is well-documented, accessible, and compliant with standards.
• Pipeline Automation & Optimization: Drive automation of data pipelines and workflows to improve efficiency and reliability.
• Team Management: Mentor and grow a team of data engineers, ensuring alignment with business goals, delivery timelines, and technical standards.
• Cross-Company Collaboration: Work closely with business stakeholders, including data scientists, finance analysts, MI, and cross-functional teams, to ensure seamless data access and integration.
• Cloud Management: Lead efforts to integrate and scale cloud data services on AWS, optimizing costs and ensuring platform resilience.
• Performance Monitoring: Establish monitoring and alerting solutions to ensure high performance and availability of data pipelines and systems, preventing impact on downstream consumers.

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Created on 05/05/2025 by TN, United Kingdom

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