Description

1. Design and implement scalable cloud data platforms using AWS, Databricks, and Snowflake.
2. Build and optimize Lakehouse architectures leveraging Medallion (Bronze/Silver/Gold) patterns, Delta Lake, and modern data warehousing principles.
3. Develop and maintain batch, streaming, and real-time data pipelines using Spark, PySpark, SQL, Airflow, Glue, and AWS services.
4. Establish enterprise data governance through Unity Catalog, Snowflake Governance, metadata management, lineage, and data quality frameworks.
5. Architect secure, scalable, and cost-efficient solutions across AWS services including S3, Lambda, Glue, MWAA, Kinesis, and IAM.
6. Design semantic data models, curated data products, and reporting layers supporting Power BI, Tableau, Domo, and self-service analytics.
7. Lead AI/GenAI initiatives utilizing Databricks Genie, Snowflake Cortex, Cortex Analyst, Cortex Search, Amazon Bedrock, OpenAI, and enterprise AI platforms.
8. Implement RAG, vector search, AI agents, and conversational analytics solutions to improve business decision-making and productivity.
9. Collaborate with business, product, engineering, and analytics teams to translate requirements into scalable technical solutions.
10. Define architecture standards, best practices, and modernization roadmaps for enterprise data and AI platforms.
11. Drive platform performance, reliability, observability, security, and FinOps optimization initiatives.
12. Mentor engineers and architects while providing technical leadership for complex enterprise programs

Key Skills

Education

Bachelor's degree