• Lead the design, implementation, and optimization of enterprise-scale Azure data platform solutions • Architect data ingestion pipelines from diverse sources including databases, APIs, files, and streaming systems • Design and optimize ETL/ELT processes using SQL, Python, Spark, and Azure native services • Establish data quality frameworks, monitoring, and alerting systems • Implement data governance frameworks, security protocols, and compliance standards

• Lead the design, implementation, and optimization of enterprise Snowflake data platform solutions • Drive cloud-native data architecture initiatives and establish scalable analytics capabilities. • Design and implement enterprise-scale Snowflake data platform architectures • Establish modern data lakehouse patterns using Snowflake's cloud data platform capabilities • Define data governance, security, and access control frameworks • Lead platform assessments, optimization initiatives, and strategic planning • Architect robust data ingestion pipelines using Snowpipe, external stages, and partner integrations • Design efficient ELT processes using Snowflake SQL, Snowpark, and Python/Scala • Implement data sharing capabilities, marketplace integrations, and cross-cloud connectivity • Establish monitoring, alerting, and cost management best practices • Design integrations with BI tools, data visualization platforms, and analytics applications • Enable advanced analytics and machine learning workflows using Snowpark ML • Implement real-time and batch data processing patterns • Lead data platform migrations and modernization projects

• Design and implement data ingestion pipelines using CDC/CT (Change Tracking) from SQL Server sources • Build and maintain medallion architecture (Bronze → Silver → Gold) data layers in Snowflake • Develop data transformation models using tools like dbt, AWS Glue, or Snowflake native features (stored procedures, tasks) • Implement SCD Type 1 and Type 2 patterns for historical data tracking • Design and implement Row-Level Security (RLS) for multi-tenant data access • Create and optimize Snowflake objects including tables, views, streams, tasks, and stored procedures • Design data models supporting complex reporting requirements for absence management, workforce analytics, and substitute tracking • Ensure Gold layer is optimized for BI tool consumption (Power BI, Tableau, or other reporting tools) • Establish data quality checks, monitoring, and alerting mechanisms • Document technical designs, data dictionaries, and lineage documentation • Mentor junior team members and conduct code reviews

• Analyze and interpret complex schemas across multiple source system databases to support migration and integration requirements. • Develop conceptual, logical, and physical data models aligned with business rules, performance expectations, and regulatory constraints. • Define entity relationships, primary/foreign key structures, indexing strategies, and normalization/denormalization approaches. • Collaborate with product SMEs to map configuration and transactional datasets for a structured two‑phase migration process. • Establish robust validation frameworks covering schema compatibility, data type integrity, truncation detection, and referential consistency. • Design and build scalable, reliable data pipelines to support warehousing, analytics, and migration workflows. • Apply core data engineering principles—data modeling, partitioning, indexing, and performance tuning—independent of specific tools or technologies. • Troubleshoot data and pipeline issues efficiently and operate independently with minimal supervision. • Leverage cloud platforms (AWS, Azure, or GCP) for data services, storage, and compute infrastructure as needed.