• Implement real-time data pipelines with MQTT and Redpanda for stream processing. • Implement offline data pipelines using Dagster for batch processing. • Parse and process binary message formats from various data sources. • Build data warehouses using Postgres, Apache Iceberg, Parquet, and S3. • Design data models that allow for high-performance queries. • Validate and normalize data sources. • Improve local development and CI/CD using modern tooling and GitHub Actions.

• Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale • Design and build production LLM pipelines and infrastructure that power intelligent operations. • Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance • Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation. • Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened. • Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features. • Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices

• Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale • Design and build production LLM pipelines and infrastructure that power intelligent operations. • Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance • Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation. • Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened. • Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features. • Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices

• 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