Req ID: 364926

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a Data Engineer - Streaming (WkStream 2 - Kafka) to join our team in Bangalore, Karnātaka (IN-KA), India (IN).

Job Duties: NTT DATA is seeking a Streaming Integration Engineer to own the two streaming ingestion workstreams of the PNC Bank Hadoop-to-Iceberg POC. This role is responsible for designing and delivering production-grade PySpark Structured Streaming pipelines that ingest data into Apache Iceberg tables — operating under specific technical constraints. For example, workstream 2 requires building a Confluent Kafka-to-Iceberg ingestion application using only Apache-supported APIs. PNC will not permit the use of the unsupported Confluent Iceberg Sink Connector. Additionally, workstream 3 requires delivering a syslog-ng-to-Iceberg batch ingestion pipeline via rolling log files, as syslog-ng has no native Iceberg sink.

The engineer will work closely with GitHub CoPilot to scaffold, iterate, test, and document the streaming application code — acting as the technical reviewer and subject matter expert who ensures AI-generated pipelines are production-ready, PNC-compliant, and correctly integrated with the Iceberg catalog and Protegrity tokenization layer.

Workstream 2 – Confluent Kafka to Iceberg

Design and implement a PySpark Structured Streaming application that reads from Confluent Kafka topics, parses JSON and Avro payloads, applies schema mappings, and writes atomically to Iceberg tables using the Iceberg Spark runtime and foreachBatch micro-batch pattern

Ensure all functionality relies exclusively on public Apache-supported APIs — Apache Spark, Apache Kafka, and Apache Iceberg — with no unsupported Confluent connectors or proprietary sinks

Configure Kafka source parameters: bootstrap servers, consumer group IDs, offset management (startingOffsets, failOnDataLoss), checkpoint paths, and trigger intervals

Implement PII detection and Protegrity tokenization hooks within the ingestion pipeline before data lands in the Iceberg Bronze layer

Write comprehensive unit and integration tests: row count validation, schema conformance checks, Kafka offset commit verification, and data comparison against the source topic

Support PNC UAT — walk PNC engineers through the code, demonstrate no unsupported connectors are used, and address review findings

Minimum Skills Required: Apache Kafka – Producer & Consumer

4+ years of hands-on experience with Apache Kafka, including both producer and consumer development in PySpark, Java, or Scala

Deep understanding of Kafka internals: topics, partitions, consumer groups, offsets, rebalancing, and exactly-once delivery semantics

Experience with Confluent Kafka: schema registry, Avro/JSON serialisation, and Confluent Cloud or on-prem cluster configuration

Proven ability to build ingestion pipelines without relying on unsupported or third-party sink connectors — using only native Kafka consumer APIs and Spark integration

Familiarity with Kafka Connect architecture to evaluate trade-offs and articulate why application-level ingestion is preferred in constrained environments

PySpark Structured Streaming

Strong practical experience with PySpark Structured Streaming: Kafka source, file source, foreachBatch, output modes (append/update/complete), and checkpoint management

Experience tuning streaming micro-batch trigger intervals, watermarking, and late data handling for production workloads

Hands-on experience writing streaming data directly to Apache Iceberg tables using the Iceberg Spark runtime

Ability to implement robust error handling: dead-letter queues, parse error isolation, and recovery from checkpoint failures

Data Engineering & Iceberg

Working knowledge of Apache Iceberg: catalog configuration, schema definition, append writes, and partition strategy for event and log data

Familiarity with S3-compatible object storage as an Iceberg warehouse destination

Understanding of medallion architecture — ability to correctly land streaming data in the Bronze layer with appropriate schema governance

About NTT DATA

NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.

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