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NTT DATA Logo NTT DATA · IT - Software Development

Offshore Pod Leads

India, TBD · Job Posted May 20, 2026
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Job Description

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.

Job Responsibility

  • 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
  • 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
  • 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
  • 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
  • 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
  • Audit and catalog all existing AWS Glue jobs — including PySpark and Python shell scripts, Glue Crawlers, Glue Data Catalog configurations, triggers, and job bookmarks
  • Assess Redshift loading patterns (COPY commands, stored procedures, views, materialized views) and define equivalent target-state patterns in Databricks using Delta Lake MERGE, upsert, and partition strategies
  • Evaluate and migrate Glue Crawlers and Glue Data Catalog schemas to Databricks Unity Catalog, ensuring metadata consistency and lineage continuity
  • Redesign Glue workflows and triggers as Databricks Workflow DAGs, preserving scheduling intent while improving observability and retry logic
  • Collaborate with AWS and cloud infrastructure teams to manage IAM role transitions, S3 access patterns, and network configurations during and after migration
  • Validate migrated pipelines against Glue/Redshift source outputs, including Redshift audit tables, row counts, and business-critical KPI reconciliation
  • Assess and migrate Amazon Kinesis Streams-based ingestion pipelines — analyzing stream consumers, shard configurations, and downstream processing logic — and re-architect them using Databricks Structured Streaming with Delta Lake as the target sink
  • Design low-latency streaming pipeline patterns on Databricks (Auto Loader, Structured Streaming) to replace Kinesis consumer applications, ensuring at-least-once or exactly-once delivery semantics are preserved

Requirements

  • 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 building production-grade ETL/ELT pipelines
  • Hands-on experience with Databricks (Spark clusters, notebooks, Jobs/Workflows, Delta Lake) — Databricks certification preferred
  • Solid understanding of AWS data services: S3, Redshift, Glue Data Catalog, IAM, CloudWatch
  • Experience migrating or re-platforming Informatica workloads to a modern data platform (Databricks, Spark, or cloud-native ETL)
  • Proficiency in SQL and familiarity with Redshift-specific SQL dialects and optimization patterns
  • Familiarity with Unity Catalog, Delta Live Tables, or similar data governance/pipeline orchestration frameworks is a strong plus
  • Experience with CI/CD tooling (Git, GitHub Actions, Jenkins, or similar) applied to data pipeline development
  • Proven track record leading a team of 4+ data engineers in a delivery-focused engagement or program
  • Strong analytical and problem-solving skills with the ability to work through ambiguous, undocumented legacy systems
  • Excellent written and verbal communication
  • able to present technical findings and migration plans to client stakeholders
  • Experience working in Agile/Scrum delivery environments
  • Consulting or client-engagement experience is a significant advantage
  • 12+ years in data engineering with at least 5+ years of hands-on AWS Glue experience (PySpark ETL scripts, Python shell jobs, Glue Studio, Crawlers, Data Catalog, job bookmarks, triggers)
  • Deep expertise in Amazon Redshift — data modeling, distribution/sort keys, COPY/UNLOAD operations, stored procedures, performance tuning, and Redshift Spectrum
  • Hands-on experience with Amazon Kinesis Streams — stream consumers (KCL/Lambda/Glue Streaming), shard management, retention policies, and integration with downstream AWS services
  • Strong AWS platform proficiency: S3, IAM, CloudWatch, Kinesis Streams, AWS Secrets Manager, Lake Formation — AWS Solutions Architect or Data Analytics certification preferred
  • Strong proficiency in PySpark and Spark SQL for building and optimizing production pipelines on Databricks
  • Hands-on experience with Databricks (Spark clusters, notebooks, Jobs/Workflows, Delta Lake) — Databricks certification preferred
  • Experience migrating Glue-based workloads to Databricks or equivalent Spark-based platforms
  • Familiarity with Unity Catalog, Delta Live Tables, and Databricks Asset Bundles is a strong plus
  • Experience with CI/CD tooling (Git, GitHub Actions, AWS CodePipeline, or similar) applied to data pipeline development
  • Proven track record leading a team of 4+ data engineers in a delivery-focused engagement or program
  • Strong ability to navigate AWS service interdependencies and translate cloud infrastructure nuances into migration decisions
  • Excellent written and verbal communication
  • able to present technical migration plans and risk assessments to client stakeholders
  • Experience working in Agile/Scrum delivery environments
  • Consulting or client-engagement experience is a significant advantage

Nice to have

  • Experience with real-time or near-real-time streaming pipelines using Databricks Structured Streaming, Delta Live Tables, or Apache Kafka — particularly migrating from Amazon Kinesis-based architectures
  • Databricks Certified Data Engineer Associate or Professional certification
  • AWS Certified Data Analytics – Specialty or AWS Certified Solutions Architect
  • Prior experience on a large-scale ETL migration or data platform modernization program
  • Familiarity with data observability tools (Monte Carlo, Great Expectations, Deequ) or Databricks built-in data quality frameworks
  • Experience with infrastructure-as-code tools such as Terraform or AWS CDK for managing Databricks workspace configurations
  • Knowledge of data mesh principles, medallion architecture (Bronze/Silver/Gold), and lakehouse design patterns
  • Prior consulting, systems integration, or professional services delivery experience
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