Role Summary
NTT DATA is seeking experienced Data Engineers 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 engineer will harden, extend and operate reusable migration frameworks developed during the POC, implement Spark/PySpark migration pipelines, integrate Cyber/tokenization services, implement migration controls and automated reconciliation, optimize Iceberg storage and troubleshoot PB-scale migration workloads.
This is a highly hands-on engineering role requiring strong distributed-data-processing fundamentals and experience building reliable, restartable and high-performance data pipelines.
Experience
10+ years of Data Engineering experience, including 4+ years hands-on production experience with Apache Spark/PySpark.
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Key Responsibilities
Migration Framework Engineering
. Harden and extend a config-driven Python/PySpark migration framework capable of migrating thousands of Hadoop/Hive tables without table-specific code.
. Build reusable Hive metadata/schema discovery and migration-configuration components.
. Implement migration strategy selection including CTAS/bulk, partition-wise and chunk-wise processing based on table size, partition/file characteristics, tokenization requirements and execution history.
. Implement PNC-defined Data Movement Controls including:
o checkpointing,
o restart/recovery,
o idempotency,
o retries,
o control totals,
o audit logging,
o exception handling.
PB-Scale Migration Execution
. Execute Hadoop/Hive → Apache Iceberg historical migrations at multi-TB/PB scale.
. Design migration patterns for small, medium, large and very-large tables.
. Tune parallel Spark execution across tables, partitions and chunks.
. Implement resumability so failed large-table migrations can restart from safe checkpoints rather than reprocessing entire tables.
. Diagnose and optimize Hadoop-read, Spark-processing, tokenization and Iceberg-write bottlenecks.
. Support migration-wave execution and production issue resolution.
Cyber / Tokenization Integration
. Integrate PNC-approved tokenization capabilities, including TaaS/Protegrity where applicable, into migration pipelines.
. Implement secure processing of sensitive fields.
. Handle service failures, retries, throttling and restart scenarios.
. Measure and optimize tokenization impact on end-to-end migration throughput.
. Support technical validation of Cyber/tokenization outcomes based on PNC-defined rules.
Apache Iceberg Engineering
. Create/configure Iceberg tables using approved target standards.
. Implement appropriate partitioning and file-sizing strategies.
. Optimize compaction and small-file handling.
. Work with Iceberg snapshots, schema evolution and metadata.
. Troubleshoot Iceberg write/commit/catalog issues.
. Support table maintenance and performance optimization.
Data Validation & Reconciliation
. Develop automated source-target validation covering:
o row counts,
o schema conformity,
o partition completeness,
o control totals,
o aggregates/checksums,
o null/duplicate checks,
o tokenization validation where applicable.
. Isolate reconciliation failures at table/partition level.
. Implement selective retry/reprocessing of failed data.
. Produce auditable migration evidence.
Production Engineering
. Monitor and troubleshoot distributed Spark jobs.
. Analyze Spark UI, executor logs and resource utilization.
. Troubleshoot Hadoop/HDFS, Spark, Iceberg catalog, object-storage and tokenization issues.
. Build operational telemetry covering migration status, throughput, retries and failures.
. Participate in code reviews, testing, CI/CD and engineering standards.
. Mentor mid-level/junior Data Engineers.
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Mandatory Skills
Strong hands-on experience with:
. Python
. PySpark
. Apache Spark / Spark SQL
. Hadoop / HDFS
. Hive / HiveQL / Hive Metastore
. SQL
. Apache Iceberg
. Parquet
. Linux
. Git
. distributed data processing
. large-volume ETL/data migration
. source-target reconciliation
. Spark performance tuning.
Practical Iceberg experience should include:
. table creation/read/write,
. partitioning,
. snapshots,
. schema evolution,
. file sizing,
. compaction,
. Spark integration.
Strong understanding of:
. parallelism,
. checkpoint/restart,
. idempotency,
. retry/recovery,
. large-table processing,
. small-file problems,
. distributed-system troubleshooting.
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Preferred Skills
. Apache Polaris/Nessie/Glue or another Iceberg catalog.
. Kubernetes/OpenShift/Spark Operator.
. S3 or S3-compatible object storage NetApp StorageGRID/ONTAP preferred.
. TaaS/Protegrity or equivalent tokenization/masking technology.
. PostgreSQL for migration configuration/control/audit metadata.
. Trino.
. Control-M/Airflow or equivalent enterprise scheduler.
. CI/CD for Python/Spark applications.
. Banking/financial-services experience.
. PB-scale migration experience.
. HiveQL→Spark SQL modernization experience.
. Bronze/Silver/Gold lakehouse experience.

Data Engineer Advisor