Senior Data Engineer

Role Summary

NTT DATA is seeking a highly experienced Senior Data Engineer / Technical Lead for a large-scale banking data modernization program involving migration of approximately 2 PB of historical data from Cloudera Hadoop/Hive to Apache Iceberg.

The role requires a strong hands-on engineer who can lead the engineering team while continuing to design, develop, troubleshoot and optimize the solution. The candidate will own technical execution of the migration framework, Spark/PySpark engineering, Iceberg implementation, data modelling/schema mapping, Cyber/TaaS integration, migration controls, reconciliation, performance tuning and technical delivery of migration waves.

This is not a coordination-only Technical Lead role. The candidate is expected to contribute to code, conduct design/code reviews, troubleshoot complex production issues and mentor Data Engineers.

Experience

10+ years of Data Engineering experience, including:

• 5+ years hands-on Apache Spark/PySpark.

• Strong Hadoop/Hive experience.

• Experience leading Data Engineering teams.

• Large-scale data migration/data modernization experience.

• Strong data modelling and source-to-target mapping experience.

Multi-hundred-TB/PB-scale migration experience is strongly preferred.

Key Responsibilities

Technical Leadership

• Lead a team of Data Engineers through assessment, development, migration, validation and production execution.

• Translate architecture and migration standards into detailed engineering designs and reusable implementation patterns.

• Own technical estimates, engineering backlog, work allocation and technical delivery.

• Conduct design and code reviews and enforce engineering standards.

• Mentor Data Engineers and provide hands-on technical guidance.

• Act as the primary engineering escalation point for complex migration and production issues.

• Partner with Data Architect, Technical PM, security, infrastructure and PNC technical teams.

• Lead root-cause analysis and ensure fixes are incorporated into reusable framework patterns rather than implemented as one-off solutions.

Hadoop-to-Iceberg Migration

• Lead technical execution of approximately 2 PB of Hadoop/Hive historical-data migration to Apache Iceberg.

• Harden and extend the reusable, configuration-driven migration framework developed during the POC.

• Analyze Hive/HDFS metadata, tables, partitions, file layouts and migration complexity.

• Implement migration strategies including bulk/CTAS, partition-wise and chunk-wise processing.

• Design execution approaches for small, medium, large and very-large tables.

• Optimize parallel migration across tables and partitions.

• Implement checkpointing, restart/recovery, idempotency and safe retries.

• Lead technical execution and troubleshooting of migration waves.

Data Modelling & Schema Engineering

• Analyze existing Hive data models and translate them into approved Apache Iceberg physical models.

• Develop and review conceptual/logical/physical data models where required, with primary emphasis on physical modelling for migration.

• Perform source-to-target data mapping and schema/data-type mapping.

• Define and implement table structures consistent with approved Iceberg standards.

• Analyze:

o entities and relationships,

o primary/foreign-key relationships,

o data types,

o partition keys,

o business keys,

o reference/cross-reference structures.

• Identify schema incompatibilities and recommend remediation.

• Work with the Data Architect on partitioning, file-layout and model-design decisions.

• Ensure migrated models preserve required business relationships and downstream compatibility.

• Support future Bronze/Silver/Gold modelling requirements where applicable.

Important: enterprise modelling standards remain owned by the Data Architect; the Senior DE/Technical Lead is responsible for detailed modelling and implementation within those standards.

Spark/PySpark Engineering & Performance

• Develop and review production-grade Python/PySpark components.

• Tune Spark jobs for PB-scale migration.

• Diagnose Spark stages, executors, shuffle, skew, memory and I/O bottlenecks.

• Optimize table/partition/chunk parallelism.

• Tune workloads to meet migration-throughput objectives.

• Identify bottlenecks across:

Hadoop Read → Spark → TaaS/Tokenization → Iceberg Write → Reconciliation

• Coordinate technical remediation with infrastructure/storage/network teams where bottlenecks are outside the migration framework.

Apache Iceberg Engineering

• Implement approved Iceberg table standards.

• Develop/configure Iceberg tables and Spark→Iceberg processing.

• Implement:

o partitioning,

o schema evolution,

o snapshots,

o file sizing,

o compaction,

o metadata maintenance,

o small-file remediation.

• Troubleshoot Iceberg catalog/write/commit issues.

• Optimize Iceberg table performance and storage characteristics.

Cyber-3 / Tokenization

• Implement PNC-approved TaaS/Protegrity tokenization and retokenization patterns.

• Integrate tokenization into Spark migration pipelines.

• Implement batching, retry, timeout and recovery handling.

• Troubleshoot tokenization failures/performance bottlenecks.

• Measure impact of tokenization on end-to-end migration throughput.

• Support Cyber/tokenization validation based on PNC-defined rules.

Migration Controls & Reconciliation

• Lead implementation of PNC Data Movement Controls, including:

o checkpointing,

o idempotency,

o control totals,

o restart/recovery,

o retry,

o audit logging,

o exception handling.

• Build reusable source-to-target reconciliation capabilities covering:

o row counts,

o schema validation,

o partition completeness,

o control totals,

o aggregates,

o hash/checksum where appropriate,

o null/duplicate validation,

o tokenization validation.

• Isolate discrepancies at table/partition level.

• Enable selective reprocessing rather than full-table reruns.

• Produce technical evidence required for migration acceptance.

Mandatory Skills

Strong hands-on expertise in:

• Python

• PySpark

• Apache Spark / Spark SQL

• Hadoop / HDFS

• Hive / HiveQL / Hive Metastore

• SQL

• Apache Iceberg

• Data modelling

• Source-to-target mapping

• Parquet

• Linux

• Git

• Large-volume ETL/data migration

• Data validation/reconciliation

• Spark performance tuning

• Distributed-system troubleshooting.

Data Modelling

Strong experience with:

• logical and physical data modelling,

• source-to-target mapping,

• schema/data-type mapping,

• entity relationships,

• keys and referential relationships,

• dimensional/relational modelling concepts,

• large-scale analytical data models.

Technical Leadership

Demonstrated experience:

• leading Data Engineering teams,

• technical estimation/planning,

• detailed solution design,

• code/design reviews,

• mentoring engineers,

• production troubleshooting,

• root-cause analysis,

• communicating technical issues with architects/customer teams.

Preferred Skills

• Apache Polaris/Nessie/Glue.

• OpenShift/Kubernetes/Spark Operator.

• S3-compatible object storage.

• NetApp StorageGRID/ONTAP.

• TaaS/Protegrity or equivalent tokenization technology.

• Trino.

• PostgreSQL.

• Control-M/Airflow.

• CI/CD for Spark/Python applications.

• Banking/financial-services experience.

• PB-scale data migration.

• Bronze/Silver/Gold modelling and implementation.

• HiveQL→Spark SQL modernization.