Data Architect

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Hands on - Lead Data Architect to define and govern the end-to-end enterprise data architecture for a modern, scalable Cloudera-based Lakehouse platform. This role demands technical competency and capabilities across multiple domains including CDC pipelines, Kafka streaming, real-time marts, MLOps/LLMOps frameworks, and data virtualization.
You will act as the Design Authority, ensuring architectural consistency, reusability, and compliance across all initiatives.

Define and own the target state data architecture aligned with business and regulatory needs.

 * Serve as design authority across CDC ingestion, data lake zones (raw, curated, real-time), consumption layers, and AI enablement.
 * Lead architecture governance reviews, ensure solution alignment to enterprise reference architecture.
 * Deep expertise in Cloudera CDP,Apache Iceberg, Hive, Impala, Kafka, NiFi, Airflow.

 * Drive adoption of open table formats (e.g., Iceberg) and cataloging strategies.
 * Evaluate and integrate tools for data quality, observability, lineage, and security (e.g., Ranger).
 * Collaborate with CDO, CIO, and business units to translate enterprise goals into data capabilities.
 * Support platform scalability, performance optimization, cost modeling, and cloud readiness.
 * Coach teams and create reusable patterns and blueprints.

 * Guide ingestion architecture using Oracle GoldenGate, real-time log capture, schema evolution, and reconciliation.
 * Design Kafka-based ingestion, stream processing, and event-driven microservices.
 * Architect real-time serving layers for digital channel use cases (e.g., customer 360, fraud, personalization).
 * Define architecture for operationalizing ML and GenAI models, including feature stores, CI/CD for models, and prompt/data governance.
* Drive adoption of Denodo or similar tools for agile data access and abstraction.