Jobs at AgileEngine

Designs, deploys, and manages scalable GCP infrastructure, Kubernetes workloads, and CI/CD pipelines. Responsibilities include provisioning resources with Terraform, operating GKE and Helm-based microservices deployments, managing serverless workloads on Cloud Run and Cloud Functions, configuring storage and load balancing, optimizing performance and cloud costs, and implementing DevSecOps security and compliance checks. The role also requires automation using Azure DevOps, Bash, PowerShell, or Python.

Lead roadmap enhancements for an enterprise loyalty platform, owning product vision, business logic, platform rules, backlog prioritization, dependencies, and release milestones. Translate complex requirements into precise, reusable specifications for business, technical, automated, and AI-assisted development workflows. Collaborate with stakeholders and delivery teams to resolve challenges, communicate progress, and guide product decisions across enterprise clients and industries.

Build and maintain React micro-frontend applications for a high-scale payments platform. Develop payment methods, deliver production features and bug fixes, improve reliability, and create comprehensive unit, integration, end-to-end, and accessibility tests. Work with TypeScript, Webpack Module Federation, GraphQL, Apollo Client, CI/CD pipelines, distributed systems, telemetry, feature flags, accessibility, internationalization, and backend API contracts.

Design and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data engineering automation, implement data quality and monitoring controls, optimize workloads, and support testing, deployment, troubleshooting, and production operations. Collaborate with business, data engineering, and platform teams to identify automation opportunities and develop reusable frameworks.

Execute large-scale data platform migrations to Snowflake, adapting approximately 1,000 datasets and existing pipelines. Develop, test, schedule, and monitor Airflow workflows while supporting migrations to Airflow 3, Iceberg, and new Confluent regions. Ensure data quality, reliability, performance, and timely completion of modernization initiatives through hands-on Python, SQL, Spark, Snowflake, Kafka, and AWS S3 engineering.