Roles and Responsibilities

• Design and architect enterprise-grade data platforms on AWS or Databricks to support analytics, AI/ML, and business intelligence use cases.
• Build repeatable and reusable frameworks for data ingestion, transformation, quality, observability, and governance.
• Lead initiatives involving Data Lakes, Data Warehouses, and Real-time Streaming Architectures (IoT, Kinesis, Kafka).
• Collaborate with data scientists, data engineers, and application developers to build end-to-end GenAI and ML pipelines.
• Optimize performance of services like EMR and Spark, tuning cluster configurations and workloads.
• Implement enterprise-wide data quality, data observability, and metadata management solutions.
• Architect robust API integrations and microservices to enable scalable data access and distribution.
• Contribute to Agile delivery practices, providing architectural leadership and mentoring for cross-functional teams.

Required Qualifications

• 12+ years in Enterprise Data Architecture, with a strong focus on modern data platforms.
• 5+ years of hands-on experience with AWS Data & Analytics stack (e.g., S3, Glue, Athena, Lake Formation, EMR, Redshift, Kinesis, OpenSearch).
  • OR strong equivalent experience in Databricks.
• 5+ years in Data Warehousing, Data Lakes, and Data Modeling techniques.
• 5+ years of programming experience using Python, Spark, R, SQL, or equivalent languages.
• 3+ years implementing Data Quality and Observability frameworks.
• 3+ years building Real-time / Streaming data solutions for IoT or similar domains.
• 3+ years working with APIs, microservices, and application integration patterns.
• 3+ years of experience in Data Science, Statistics, Machine Learning, and Generative AI.
• Proficient in performance tuning and workload optimization, especially for Spark on EMR or Databricks.
• Strong understanding of cloud-native architecture, data governance, and secure data sharing strategies.

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