Teams at Version 1

Lead end-to-end delivery of enterprise data engineering workstreams, designing and operating scalable batch and streaming pipelines on AWS, Databricks, and Spark. Provide technical governance, design oversight, stakeholder leadership, and mentoring. Build transformations with dbt, orchestrate workflows with Airflow, and ensure systems are secure, tested, observable, reliable, and cost-efficient. Troubleshoot production issues, optimize performance and data quality, and contribute to estimation and commercial discussions.

Designs, builds, and operates scalable batch and streaming data platforms for enterprise and regulated clients. The role develops high-throughput data systems using AWS, Databricks, Spark, dbt, and Airflow; applies data modelling, testing, security, governance, observability, and performance optimization; supports production systems; and collaborates with stakeholders, analytics, product, and platform teams.

Design, build, and ship production-grade AI-powered full‑stack applications: React frontends, Python backends, integrate with LLMs/agentic workflows, implement CI/CD (Kubernetes/Terraform), ensure security, observability, and take prototypes to production while advising and mentoring teams.