Lead Data Engineer

• Team Management: Manage the on-site team of 3-4 Data Engineers
• Roster & Scheduling: Create, manage, and maintain team rosters and schedules to ensure full 8-hour coverage, 7 days a week, including rotational weekend and holiday coverage
• Availability: Must be able to work a day shift and manage a team that provides 7-day-a-week coverage, including weekends and holidays on rotation
• Coverage Assurance: Handle all team leave requests (planned and unplanned). You are responsible for ensuring coverage is always maintained, especially during medical or other unexpected absences
• Performance & SLAs: Act as the primary operational point of contact, ensuring the team executes all tasks efficiently, makes sound decisions, and adheres to all agreed-upon SLAs
• Final Escalation Point: Serve as the final technical escalation point for the team
• personally handle the most critical job failures and system issues
• Expert RCA: Lead all high-priority Root Cause Analysis (RCA) efforts, documenting solutions and implementing permanent fixes to prevent recurrence
• Strategic Improvement: Drive the team's efforts in performance tuning and AWS Glue cost optimization
• Automation Strategy: Define and lead the development of new automation jobs (using Glue, Lambda, and Step Functions) to reduce manual support tasks
• Complex Data Management: Oversee all data integrity and synchronization tasks, including CDC reloads and write-backs to external databases like Teradata
• Knowledge Transfer: Own the knowledge transfer process to internal resources and ensure the team maintains excellent documentation and runbooks

• Demonstrated expertise with a minimum of 8 years of relevant experience in data engineering with experience of leading and managing a technical support or data engineering team
• Ability to function as a player-coach — leading while also contributing hands-on
• Proficiency in Python for data engineering, ETL/ELT workflows, and automation tasks
• Solid understanding of data governance, security, and performance optimization practices
• Experience designing, creating and maintaining data pipelines in AWS environments, with strong exposure to AWS Data services like S3, Glue, Lambda, and Step Functions, and other related services
• Data Architecture: Expert-level understanding of ETL, data warehouse design, and pipeline optimization
• Database Expertise: Deep experience with Teradata, Change Data Capture (CDC) processes, and data synchronization
• Integration: Experience with Lambda-based integrations, such as triggering SAS or RMJ events
• Strong communication skills and ability to translate complex technical concepts for business stakeholders
• Resilience: Ability to make critical, autonomous decisions and lead a team in a high-pressure environment

Preferred Skills: AWS Certified Solutions Architect