Employer description & source details
Build and maintain the tooling and infrastructure that lets data champions ship pipelines autonomously Own monitoring, alerting, and dataops practices that keep the platform reliable as it scales Operate Kafka as the message broker powering cross-team event-driven architecture Deploy, own and maintain infrastructure components (services, OSS helm charts) with Terraform and Kubernetes Run high-throughput, low-latency processing on Databricks/Spark and cloud-native services on AWS Support dbt for transformations and Airflow (or similar) for orchestration across the platform Partner with product, analytics, and infrastructure teams on design and delivery of platform improvements Troubleshoo t production issues and drive the fixes Embrace and champion the thoughtful adoption of AI to improve team performance and business outcomes. Leverage AI tools (e.g., generative AI, automation platforms, data copilots) to improve productivity, decision-making, and output quality in your day-to-day work. Bachelor’s or Master’s degree in Computer Science , Engineering, or related field 5+ years building data or platform engineering systems in production Strong coding skills in Python or Java Experience operating production data platforms on AWS and supporting downstream teams who build on them Hands-on with Kubernetes and Terraform Experience running Kafka (or a similar broker) in event-driven systems Working knowledge of Databricks/Spark, dbt, and Airflow Solid grasp of data modeling, data quality, and relational and non-relational databases (e.g., Postgres, SQL Server) Comfortable with CI/CD, testing frameworks, and observability tooling Experience with d ashboarding technologies Experience with streaming engine ( flink , kdb ) Full lifecycle development of a commercial system, includin
Extracted skills: python, java, sql, aws, kubernetes, terraform, finance