• Assess legacy data and reporting workloads to identify where automation replaces manual effort. • Lead the migration of Informatica-based data workloads to Databricks, ensuring performance, reliability, and data integrity. • Design and execute an automation-first modernization strategy for data pipelines, reporting systems, and analytics platforms. • Apply tool-assisted and AI-assisted techniques to accelerate modernization while maintaining compliance and control. • Build repeatable frameworks and patterns for: Data ingestion, transformation, and orchestration; Reporting and analytics modernization; Data validation, reconciliation, and quality controls. • Establish governance to ensure modernization efforts are consistent, auditable, and scalable. • Partner with distributed data teams to deploy and scale modernization patterns across environments. • Provide hands-on technical leadership while remaining engaged in execution.

• Design & implement a modern data warehouse using Amazon Redshift, ensuring scalability and performance. • Develop and maintain ETL pipelines using AWS Glue (Python/PySpark), moving data from SQL Server (RDS) and other sources. • Structure our S3 data lake for efficient storage, partitioning, and integration with Redshift. • Define data models (star schema, dimensional modeling) to support reporting and analytics. • Establish data governance—documentation, quality checks, and monitoring for pipelines. • Collaborate with BI teams to ensure the warehouse meets reporting needs (CRM, dashboards). • Optimize costs by tuning Redshift clusters, managing Glue job efficiency, and automating workflows. • Troubleshoot data infrastructure issues and optimize performance across systems. • Contribute to the long-term vision for the data platform, including new tools, workflows, and process automation. • Maintain documentation of data systems, flows, and integration strategies. • Support project planning and estimation in coordination with business and technical teams

• Design, develop, and maintain scalable data pipelines and ETL processes using Python and AWS services • Build and deploy serverless functions using AWS Lambda for data processing and automation • Implement data integration solutions connecting diverse source systems to analytics platforms • Mentor junior data engineers through pair programming, code reviews, and technical coaching • Collaborate with data analysts and business stakeholders to understand requirements and deliver solutions • Optimize data pipeline performance, reliability, and cost efficiency in AWS environments • Develop and maintain comprehensive documentation for data systems and processes • Participate in on-call rotation to support production data infrastructure • Contribute to the establishment of team coding standards and engineering best practices • Work with Git for version control and participate in collaborative development workflows

• Design, build, and maintain ETL/ELT pipelines for structured and unstructured datasets • Process and analyze customer, operational, and financial transaction data • Develop scalable data workflows and reusable scripts for automation and reporting • Maintain data models, metric definitions, and analytics documentation • Optimize SQL queries and improve data processing performance and reliability • Integrate external data sources such as ABS and Census datasets into existing databases • Support cloud-based data platforms and modern data lakehouse environments • Build dashboards and reports for internal and external stakeholders • Translate business requirements into actionable analytics and reporting solutions • Support KPI tracking, operational reporting, and customer behavior analysis • Help identify trends, anomalies, and opportunities through data-driven insights • Collaborate with engineers, analysts, and stakeholders in agile environments • Participate in planning, testing, debugging, and deployment activities • Contribute ideas to improve workflows, system reliability, and engineering processes • Adapt quickly to changing priorities and evolving business requirements • Support development of reusable templates, modular scripts, and data engineering best practices • Support additional engineering or analytics initiatives as needed • Identify opportunities to improve processes, systems, and performance • Contribute to a collaborative and high-performing team environment • Assist in maintaining data quality, governance, and documentation standards