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.