Platform Engineer

About the Role

We're looking for a Senior Engineer to join our Platform team and shape the internal tooling that empowers engineering teams across the organization. You'll design, build, and maintain high-impact developer tools — including CLI applications for environment setup, CI/CD modernization frameworks, data access libraries, and self-service platform capabilities — that directly accelerate how our engineers ship software.

What You'll Work

• Tooling & Developer Experience: Build and evolve applications that streamline environment provisioning, configuration, and day-to-day developer workflows.
• CI/CD Modernization: Design and implement next-generation continuous integration and delivery pipelines, driving adoption of modern GitOps workflows and reliable deployment practices.
• Data Access Libraries & SDKs: Develop internal libraries that provide consistent, secure, and performant access to data stores — abstracting complexity away from consuming teams.
• Internal Tooling & Automation: Identify repetitive pain points across engineering and build robust, reusable tooling to eliminate them.

Responsibilities

• Architect and deliver internal tools and libraries that are well-defined, modular, secure, scalable, and actively maintained.
• Contribute across the full development lifecycle — from architecture and prototyping through testing, development, and support.
• Drive modern software engineering practices through code and design reviews; champion DevOps principles for continuous delivery with high-quality telemetry.
• Evaluate emerging technologies through proof-of-concept work and recommend adoption where they fit our platform needs.
• Lead design discussions and align teams on architectural and strategic decisions for shared platform components.
• Mentor junior engineers through coaching, pairing, and thoughtful code/design reviews.

AI Proficienciency

• Proficient in integrating AI coding assistants, CLIs, and Model Context Protocols (MCPs) into daily development workflows.
• Uses AI effectively across the software development lifecycle — planning, design, implementation, debugging, deployment, and observability.
• Builds reusable AI tooling (agent rules, skills, internal MCPs) that compounds team productivity, while applying judgment about when AI is and isn't the right tool.
• Balances AI-driven velocity with engineering rigor — implements automated testing, structural guardrails, and CI mechanisms to validate AI-generated code and prevent technical debt.

What We're Looking For Required

• Experience: 4–8 years of professional software engineering experience building production systems at scale.
• Education: Bachelor's degree or higher in Computer Science, Engineering, or a related field.
• Python Mastery: Deep proficiency in Python — strong grasp of OOP, design patterns, packaging, dependency management, and building maintainable libraries and CLI tools(good to have).
• Software Architecture: Demonstrated ability to design well-structured, loosely coupled systems. Comfort making trade-offs between simplicity, extensibility, and performance.
• Cloud & Infrastructure: Hands-on experience with cloud platforms (Azure, AWS, or GCP).
• Container Orchestration & CI/CD: Experience with Docker, Kubernetes, and building CI/CD pipelines (GitHub Actions, Jenkins, or similar). Exposure to GitOps workflows (Flux, ArgoCD) is a plus.
• Data Access & Storage: Experience working with relational databases (PostgreSQL, MSSQL) and NoSQL/key-value stores. Ability to design clean data access abstractions.

Strongly Preferred

• Experience building internal developer tools, platform SDKs, or CLI frameworks.
• Experience with data platform technologies (Spark, PySpark, Databricks, Airflow, or Dagster).
• Exposure to modern data architectures (Delta Lake, Iceberg, data catalogs).
• Hands-on experience with observability tooling (Datadog, Grafana/Prometheus, OpenTelemetry, Statsig).
• Track record of driving developer adoption of platform tools and improving engineering productivity.

Core Competency

● Judgment: Sound decision-making with incomplete information; ability to evaluate trade-offs between cost, reliability, performance, and developer experience.

● Ownership: End-to-end accountability from problem identification through solution delivery and adoption.

● Collaboration: Clear communication across teams and geographies; empathy for internal users; effective remote collaboration.

● Initiative: Proactive identification of workflow bottlenecks and a drive to eliminate them through thoughtful tooling.