Enterprise Data and AI Architect

Key Responsibilities

• Define enterprise-wide data and AI architecture strategies, reference models, and standards
• tailored to retail banking, lending, payments, risk management, and investment banking domains.
• Design and oversee deployment of data lakes, lakehouses, and warehouses that scale to support
• regulatory reporting, fraud detection, credit risk analysis, and real-time customer engagement.
• Build and optimize large-scale data pipelines using Hadoop ecosystem (HDFS, Hive, HBase, YARN, MapReduce) and PySpark for high-volume financial transaction data.
• Implement event-driven data integration across batch and streaming platforms (Kafka, Spark Streaming, Flink) to enable near real-time insights.
• Establish robust data governance frameworks for data lineage, cataloguing, and compliance with financial regulations (RBI, SEC, GDPR, CCPA, Basel III).
• Drive adoption of cloud-native data platforms (Azure Synapse, AWS Redshift, GCP BigQuery,
• Databricks, Snowflake) for scalable analytics and AI readiness.
• Architect semantic and feature store layers that accelerate AI/ML use cases such as credit scoring,
• anti-money laundering (AML), portfolio optimization, and personalized banking.
• Provide architectural oversight for large-scale data modernization and cloud migration initiatives.
• Mentor data engineering teams on Hadoop, PySpark, streaming, and cloud-native practices within BFSI environments.
• Collaborate with enterprise, application, and infrastructure architects to ensure alignment across systems and business functions.

Preferred Candidates

• Proven experience as a Data Architect or senior data leader in banking or large-scale financial
• services organizations.
• Strong expertise in the Hadoop ecosystem (HDFS, Hive, HBase, Spark, YARN, MapReduce).
• Proficiency in PySpark for processing high-volume structured and unstructured financial data.
• Experience with cloud-native data platforms (AWS, Azure, GCP) and distributed data architectures.
• Deep knowledge of data governance, metadata management, and compliance frameworks.
• Strong understanding of relational, NoSQL, and columnar databases used in BFSI.
• Hands-on experience with Databricks, Snowflake, or Delta Lake for implementing financial-grade lakehouse solutions.
• Familiarity with event-driven data fabric architectures that enable responsive banking services.
• Exposure to AI/ML pipelines, feature engineering, and model deployment on big data platforms.
• Knowledge of semantic layer tools (dbt, AtScale) for unified banking analytics.
• Domain expertise in risk management, regulatory reporting, fraud detection, and AML.
• Experience with enterprise architecture frameworks such as TOGAF.