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.

________________________________________

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:
checkpointing,
restart/recovery,
idempotency,
retries,
control totals,
audit logging,
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:
row counts,
schema conformity,
partition completeness,
control totals,
aggregates/checksums,
null/duplicate checks,
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.

________________________________________

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.

________________________________________

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