• Own the end-to-end architecture of the data migration platform, from ingestion through validation to production deployment • Design migration infrastructure that reduces per-customer engineering effort through reusable components and standardized patterns • Build systems that evolve migrations from engineer-led to standardized and self-service • Establish automated data quality frameworks including profiling, validation, and anomaly detection • Instrument migration systems with dashboards, metrics, and alerts for observability and continuous improvement • Architect self-service import workflows for customers and internal teams • Design ingestion pipelines that align tightly with Hibernate entity models and lifecycle rules • Build intelligent error handling and feedback loops that guide non-technical users through data correction • Create Excel-based import templates with embedded validation, documentation, and formatting standards • Maintain alignment with application changes by tracking Hibernate model evolution and updating pipelines proactively • Design, build, and maintain ETL pipelines for migrating data from legacy waste management systems • Analyze legacy datasets, reverse-engineer business logic, and implement transformation workflows • Execute end-to-end customer migrations in coordination with technical and business stakeholders • Build tooling to ingest data from legacy LAN-based systems common in the industry • Optimize pipeline performance to handle large datasets efficiently and reduce migration timelines • Drive technical decisions for data infrastructure, including build vs. buy evaluations • Partner with Product, Engineering, and Customer Success to shape scalable solutions • Mentor team members on data integration and migration patterns • Create and maintain documentation, runbooks, and operational guides • Lead code reviews and knowledge sharing to raise the bar across the team

• Define and implement the end-to-end architecture for the Lakehouse, ensuring support for high-volume batch processing, real-time streaming, and GenAI/LLM initiatives using Mosaic AI and MLflow • Design solutions that prioritize data quality at the source • Establish and enforce architectural patterns that ensure reliability and trust in data assets • Implement automated testing and observability frameworks (e.g., Delta Live Tables expectations) to maintain data health • Translate complex business requirements into robust logical and physical data models • Document and maintain clear data flow architectures for applications to ensure alignment between business intent and engineering output • Lead virtual Scrum teams and provide deep technical mentorship to data engineers and developers • Advocate for Spark/Databricks best practices, conduct code reviews, and drive the adoption of CI/CD and DevOps principles in data workflows • Architect the frameworks for data lineage, security, and lifecycle management (utilizing Unity Catalog) • Ensure strict compliance with internal policies and external regulations from the point of data ingestion to final consumption • Partner directly with business stakeholders to understand their roadmap and priorities • Ensure all data and AI initiatives are strictly aligned with tangible business value • Manage the architectural roadmap to balance immediate business needs with long-term technical scalability

• Architect, implement, and maintain scalable data pipelines across core systems. • Build and manage analytics infrastructure using both open-source and commercial tools. • Ensure data quality and schema discipline for operational reliability. • Deliver clean, consistent models and dashboards utilized by multiple teams. • Collaborate with stakeholders to support analytics, business intelligence, and decision-making initiatives.

• Develop, and maintain database solutions and data warehouse architectures using MSSQL 2019/2022 and MySQL 8. • Collaborate with stakeholders to gather requirements and understand business needs for data storage and processing. • Develop and maintain ETL processes using SSIS, BIML, and DBT to ensure efficient data integration and transformation. • Implement and manage solutions within Power BI Fabric for enterprise-scale data analytics. • Write complex SQL queries, stored procedures, functions, and triggers to support application and reporting requirements. • Optimize query performance through indexing strategies, execution plan analysis, and database tuning techniques. • Troubleshoot and resolve database performance issues, data integrity problems, and system bottlenecks. • Monitor database health, perform capacity planning, and implement proactive maintenance strategies. • Develop automation scripts using PowerShell and Python to streamline database operations and maintenance tasks. • Ensure data security, backup and recovery procedures, and compliance with organizational standards. • Utilize version control tools such as Git and project management tools like Jira and Confluence or similar platforms as required. • Work effectively in a team environment, collaborating on concurrent developments and contributing to team success. • Document database architectures, ETL processes, standards, and procedures. • Stay current with database technologies, best practices, and emerging trends in data warehousing. • Provide technical guidance and support to team members and end-users as needed. • Contribute to continuous improvement initiatives related to data management and analytics infrastructure.