Offshore Pod Leads

India T&M Special Bonus:     Req ID:   360094   NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We are currently seeking a Offshore Pod Leads to join our team in TBD, Andaman and Nicobar Islands (IN-AN), India (IN). JOB DESCRIPTION Data Engineering Pod Lead Databricks Lakehouse Migration Program Two Roles: Informatica Pod Lead  |  AWS Glue Pod Lead Engagement Type Contract / Staff Augmentation or Full-Time Employee (FTE) — Open Seniority Level Lead / Architect — 12+ years of relevant experience Number of Openings 2 (one per pod) Team Size 4–6 Data Engineers per pod lead Cloud Platform AWS (Glue, Redshift, S3, Kinesis Streams, IAM, CloudWatch) Target Platform Databricks Lakehouse (Unity Catalog, Delta Lake, Workflows) Program Type Client-facing migration engagement — ETL modernization Program Context & Opportunity Our client is undertaking a large-scale data platform modernization initiative — migrating from a legacy ETL ecosystem (Informatica PowerCenter, AWS Glue, and Amazon Kinesis Streams) feeding Amazon Redshift into a unified Databricks Lakehouse architecture built on Delta Lake. This is a high-impact, high-visibility program requiring experienced technical leaders who can navigate complex legacy systems, architect modern solutions, and lead skilled engineering teams through the full migration lifecycle. We are hiring two dedicated Pod Leads — one for each legacy source domain — who will be jointly accountable for technical excellence, delivery velocity, and team development throughout the engagement. Common Responsibilities — Both Pod Leads Technical Leadership & Architecture Own the end-to-end technical design and implementation of the migration from the respective source platform to Databricks Lakehouse (Delta Lake, Unity Catalog, Databricks Workflows). Conduct thorough assessments of existing ETL jobs — analyzing lineage, dependencies, transformation logic, scheduling, and data quality rules — prior to migration planning. Define migration patterns, reusable frameworks, and coding standards adopted across the pod. Architect scalable, cost-efficient pipelines using Databricks PySpark, Spark SQL, and Delta Live Tables (DLT) as appropriate. Make and document key architectural decisions (ADRs) with clear rationale and trade-off analysis. Drive adoption of software engineering best practices: version control (Git), CI/CD, unit testing, and code review within the pod. Team Leadership & Delivery Management Directly lead a pod of 4–6 Data Engineers, providing technical mentorship, task assignment, code reviews, and unblocking day-to-day impediments. Manage sprint planning, backlog refinement, and progress tracking against migration milestones in close coordination with the Program Manager. Hold the team accountable for quality and velocity — proactively flag risks, scope changes, and dependencies before they become blockers. Conduct regular 1:1s and technical feedback sessions to support the professional growth of pod members. Foster a culture of ownership, collaboration, and continuous improvement within the pod. Client & Stakeholder Communication Serve as the primary technical point of contact for your pod's workstream with the client. Translate complex technical concepts and migration trade-offs into clear, concise communications for both technical and non-technical stakeholders. Participate in program-level status reviews, architecture governance meetings, and client steering committees as required. Manage expectations around scope, timelines, and quality, escalating issues appropriately. Quality, Governance & Documentation Ensure all migrated pipelines meet data quality, SLA, and observability requirements defined by the client. Champion data governance best practices including lineage tracking, catalog registration in Databricks Unity Catalog, and access control alignment. Produce and maintain clear technical documentation: architecture diagrams, runbooks, migration playbooks, and handover materials. Coordinate with QA/testing resources to validate migrated pipelines against source-system outputs. ROLE 1 — Informatica PowerCenter Pod Lead Role Overview The Informatica Pod Lead will own the migration of Informatica PowerCenter-based ETL jobs to the Databricks Lakehouse platform. This role demands deep expertise in Informatica's architecture, transformation logic, and metadata — paired with the ability to re-engineer complex legacy workflows into modern, cloud-native Databricks pipelines on AWS. Role-Specific Responsibilities Analyze and decompose Informatica PowerCenter mappings, sessions, workflows, and worklets to understand full transformation logic, source/target connectivity, and scheduling dependencies. Define and execute a structured migration methodology — assess, convert, validate — for translating Informatica logic into equivalent PySpark/Spark SQL code on Databricks. Identify opportunities to simplify or consolidate legacy transformations during migration rather than performing a lift-and-shift. Manage Informatica repository metadata, mapping exports (XML), and PowerCenter Designer artifacts as inputs to the migration pipeline. Coordinate with source system owners (databases, flat files, legacy APIs) to ensure source connectivity is preserved or rerouted through AWS S3/Glue Catalog during migration. Validate migrated pipelines against Informatica source outputs using row-count reconciliation, checksum comparisons, and business rule validation. Required Qualifications Technical Skills 12+ years in data engineering with at least 5+ years of hands-on Informatica PowerCenter experience (mappings, sessions, workflows, transformations, parameter files, workflow monitor). Strong proficiency in PySpark and Spark SQL for