Lead Data Engineer

Role Summary

We are looking for a Lead Data Engineer to design, build, and modernize DRL's cloud-native data platforms on Google Cloud. The role goes beyond pipeline development: the candidate will own data architecture and modeling, lead the move from legacy ETL platforms to modern cloud-native tools, and set engineering standards across internal and vendor teams. The ideal candidate combines strong hands-on skills in GCP, Informatica IICS, and Airflow with the leadership to deliver reusable, business-ready data products that support analytics and AI use cases.

Data Architecture & Data Products

• Design scalable data architectures across data lake, warehouse, and lakehouse layers on GCP.
• Lead data modeling (dimensional, canonical, and domain-driven) to deliver reusable, well-governed, business-ready data products.
Platform Modernization & Migration

• Lead the assessment, planning, and execution of migrating legacy ETL platforms (e.g., SAP BODS) to cloud-native tools such as Airflow, IICS, and PySpark.
• Define target-state architecture, migration roadmaps, and validation and reconciliation approaches to keep business running without disruption.
Cloud-Native Data Engineering

• Design and build batch and near-real-time pipelines using BigQuery, Cloud Composer/Airflow, Informatica IICS, and GCP services.
• Integrate data from enterprise sources, including SAP, using the right mix of tools and patterns.
• Optimize performance, scalability, and GCP cost.
DataOps & AI-Enabled Engineering

• Put in place CI/CD, automated testing, data quality checks, and observability for data pipelines.
• Drive automation to reduce manual operational effort and incident volumes.
• Adopt AI-assisted development practices (e.g., code generation, code migration, and automated documentation) to speed up delivery.
Leadership & Governance

• Set and enforce data engineering standards, design patterns, and best practices across teams.
• Run architecture and code reviews, and provide technical governance for internal and vendor-delivered work.
• Manage vendor and managed service partners, including delivery oversight, quality, and SLAs.
• Work with business, analytics, and IT stakeholders to turn requirements into scalable data solutions.
• Mentor engineers and build technical capability within the team.
Technical Skills

• Cloud Platform: GCP – BigQuery, GCS, Cloud Composer/Airflow, Dataproc, Cloud Functions, Pub/Sub, IAM
• Data Integration: Informatica IICS (CDI/CAI), Airflow, PySpark; SAP BODS, Talend, or similar tools
• Programming: Advanced SQL, Python, PySpark
• Data Modeling & Architecture: Dimensional modeling, data vault, lakehouse architecture, data product design
• DataOps: Git, CI/CD, automated testing, pipeline observability, Terraform (preferred)
• Data Governance: Data quality, metadata, lineage, cataloging, RBAC, and data security
• AI Enablement: AI-assisted development tools, and experience preparing data for GenAI/LLM and agent-based use cases

Function: Information Technology
Experience Level: Mid-Senior Level