Data Platform Engineering Lead -EDO

TCS•Irvine, CA

About The Position

We are seeking a highly experienced Data Platform Engineering Lead to architect, build, and manage enterprise-scale cloud-native data platforms supporting Asset Management business functions. This leadership role is responsible for driving platform engineering strategy, cloud infrastructure automation, DevOps enablement, platform reliability, security, governance, and operational excellence across the Enterprise Data Office (EDO). The ideal candidate will have deep expertise in Databricks, dbt, Apache Airflow, Enterprise Batch Tools (EBT), AWS Cloud, Terraform, Python, Kubernetes, DevOps, and Platform Engineering, with proven experience supporting enterprise data platforms in Asset Management, Investment Management, or Financial Services organizations.

Requirements

10–15+ Years of experience in Data Engineering, Platform Engineering, or Cloud Engineering
5+ Years leading enterprise Data Platform or Cloud Platform teams
Experience building enterprise-scale cloud-native data platforms
Experience in Asset Management, Investment Management, or Financial Services
Data Platform: Databricks, Delta Lake, Delta Live Tables (DLT), Unity Catalog, MLflow, Structured Streaming, Apache Airflow, Enterprise Batch Tools (EBT), dbt
Cloud: AWS (Amazon S3, AWS ECS, AWS EKS, AWS Lambda, AWS Glue, Amazon CloudWatch)
Infrastructure & DevOps: Terraform, Docker, Kubernetes, GitHub Actions, GitLab CI/CD, Jenkins, Azure DevOps, GitOps, CI/CD
Programming: Python, SQL
Operations: Site Reliability Engineering (SRE), Monitoring & Observability, Incident Management, Root Cause Analysis, Performance Optimization, Capacity Planning
Security & Governance
Platform Strategy
Technical Leadership
Architecture
Governance
Stakeholder Management
Team Mentoring
Delivery Management
Cross-functional Collaboration
Continuous Improvement

Nice To Haves

Shell Scripting (preferred)
Asset Management
Investment Management
Wealth Management
Data Governance
Regulatory Reporting
Lakehouse Architecture
DataOps
Platform Automation
Enterprise Data Warehouse
API Integration

Responsibilities

Define and execute the Enterprise Data Platform engineering strategy aligned with EDO and enterprise architecture standards.
Design, build, and manage scalable, secure, and highly available cloud-native data platforms.
Establish reusable platform frameworks, engineering standards, and accelerators.
Lead cloud modernization initiatives across enterprise data and analytics ecosystems.
Manage the complete platform lifecycle, including scalability, availability, reliability, and cost optimization.
Architect and administer enterprise Databricks environments.
Design and implement Lakehouse Architecture using Delta Lake.
Manage and optimize: Databricks Workflows, Delta Live Tables (DLT), Unity Catalog, MLflow, Structured Streaming, Cluster Policies.
Optimize platform performance, reliability, and operational costs.
Implement governance across Development, QA, UAT, and Production environments.
Design and manage cloud-native data platform solutions using: Amazon S3, AWS ECS, AWS EKS, AWS Lambda, AWS Glue, Amazon CloudWatch.
Implement secure, scalable, and highly available cloud infrastructure.
Develop reusable Terraform modules.
Automate infrastructure provisioning and configuration.
Implement Infrastructure-as-Code (IaC) best practices.
Standardize infrastructure deployment and governance.
Maintain infrastructure version control.
Ensure compliance through automated infrastructure management.
Design and implement enterprise DevOps practices using: GitHub Actions, GitLab CI/CD, Jenkins, Azure DevOps, Docker, Kubernetes, Terraform.
CI/CD pipeline design, Automated deployments, GitOps implementation, Release automation, Self-service engineering capabilities, Deployment reliability improvements.
Establish Site Reliability Engineering (SRE) practices.
Define platform SLAs, SLOs, and operational KPIs.
Implement platform monitoring, observability, and alerting.
Lead incident management, root cause analysis (RCA), and preventive actions.
Improve platform resilience and operational efficiency.
Support enterprise data platforms including: Databricks, Data Lake, Enterprise Batch Tools (EBT), Apache Airflow, Data Integration Platforms, Analytics Platforms.
Platform monitoring, Workflow orchestration, Batch processing, Data pipeline reliability, Operational support, Performance tuning.
Collaborate with: Enterprise Data Office (EDO), Chief Data Office (CDO), Portfolio Management Teams, Investment Operations, Risk & Compliance Teams, Data Governance Teams, Enterprise Architecture, Infrastructure & Cloud Engineering, Application Development Teams.
Translate business requirements into enterprise platform capabilities and drive strategic technology initiatives.