• Define the enterprise data architecture roadmap aligned with strategic business objectives. • Champion best practices for data modelling, integration, governance, and metadata management across domains. • Design canonical data models, entity relationships, and standards for schema evolution and semantic layers. • Architect modern lakehouse platforms using Databricks, Delta Lake, and Unity Catalog. • Define ingestion and transformation patterns, including orchestration using tools such as Airflow. • Implement bronze / silver / gold layering strategies for structured, trusted, and scalable data pipelines. • Establish data lineage, quality frameworks, cataloguing, and compliance processes. • Partner with engineering teams to integrate data into microservices and operational systems. • Contribute to real-time and batch pipeline designs that support analytics and ML/AI use cases. • Define and maintain data governance principles, security standards, and access controls. • Lead adoption of Unity Catalog for centralized metadata and policy enforcement. • Mentor senior engineers and act as a thought leader, influencing architecture and best practices across teams.

• Collaborate with cross-functional teams to define data requirements, perform data collection, and ensure data quality and integrity. • Curate data sets including through hands on analyses and automated tools including web scraping, ETL pipelines and SQL queries. • Conduct in-depth research and analysis to identify new data sources and complement existing data sources. • Design and maintain intuitive BI dashboards, reports, and visualizations to effectively communicate complex data and insights. • Identify actionable business insights through analysis of data trends, patterns, and correlations. • Own documentation of data processes, schemas, and data sets • Build using best practices in security, design, and data quality.

• Design and develop ETL/ELT pipelines on platforms like Databricks (PySpark, Delta Lake, SQL), Informatica, Teradata, Snowflake • Architect data models (batch and streaming) for analytics, ML, and reporting • Optimize performance of large-scale distributed data processing jobs • Implement CI/CD pipelines for Databricks workflows using GitHub Actions, Azure DevOps, or similar • Build and maintain APIs, dashboards, or applications that consume processed data (full-stack aspect) • Collaborate with data scientists, analysts, and business stakeholders to deliver solutions • Ensure data quality, lineage, governance, and security compliance • Deploy solutions across cloud environments (Azure, AWS, or GCP)

• Translate business requirements to data engineering solutions to support an enterprise scale AWS and/or Databricks based data environments. • Support designing, maintaining, automating and optimizing ETL operations ensuring data quality and efficient data management. • Design, build, and optimize scalable data solutions using a Medallion Architecture. • Manage ingestion routines for processing multi-terabyte datasets efficiently for multiple projects simultaneously. • Integrate data from various structured and unstructured sources to enable high-quality business insights. • Implement effective data management strategies to ensure data integrity, availability, and accessibility. • Identify opportunities for cost optimization in data storage, processing, and analytics operations. • Monitor and support user requests, addressing platform or performance issues, cluster stability, Spark optimization, and configuration management. • Collaborate with the team to enable advanced AI-driven analytics and data science workflows.