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.