Big Data Lead

Social network you want to login/join with:

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 in delivering complex pipelines will be significantly valuable to how D&G maintain and deliver 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, 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 team(s) in designing, developing, and maintaining highly scalable and performant data infrastructures.
• Customer Data Platform Development:Architect and 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 processing needs.
• Data Governance & Best Practices:Implement best practices for data governance, security, and data quality across our data platform. Ensure data is well-documented, accessible, and meets compliance standards.
• Pipeline Automation & Optimisation:Drive the 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 all levels of business stakeholders including data scientists, finance analysts, MI, and cross-functional teams to ensure seamless data access and integration with various tools and systems.
• Cloud Management:Lead efforts to integrate and scale cloud data services on AWS, optimizing costs and ensuring the resilience of the platform.
• Performance Monitoring:Establish monitoring and alerting solutions to ensure the high performance and availability of data pipelines and systems, preventing impact on downstream consumers.

#J-18808-Ljbffr