Teams at Sparq

Design, build, and operate end-to-end production data pipelines on GCP/BigQuery at scale. Optimize partitioning, clustering, query performance, and cost/slot management. Integrate and maintain ETL/ELT processes, legacy SQL Server reporting (stored procedures, SSRS), data validation/monitoring, and data modeling. Use intermediate Python for automation and work with BI tools (Power BI, Looker, Tableau). Champion AI tool adoption and mentor teammates while ensuring delivery and client alignment.

Design, build, and migrate large-scale batch data pipelines into Databricks. Develop and optimize production-grade Python/Spark/SQL solutions, support parallel legacy and cloud systems during migration, lead technical design discussions, use AI tools to improve workflows, ensure pipeline reliability through testing/monitoring/tuning, and participate in a rotating on-call schedule.

Design, implement, and maintain secure cloud solutions across AWS and Azure. Build policy-as-code, automation, logging, and monitoring; enforce governance and compliance using tools like AWS Config and Azure Policy. Partner with security and development teams, investigate misconfigurations, remediate issues, and recommend improvements. Champion AI tools (Claude) to streamline work and mentor teammates on best practices.