Senior Engineer – Data Systems & Platform

About the Role:

We are looking for a Senior Engineer who views data as a core software engineering discipline, not an exercise in configuring drag-and-drop SaaS vendors. In this role, you won't just glue third-party platforms together; you will own, design, and build the underlying data systems, pipelines, and internal platforms that power our production services and application backend.

You care deeply about clean code, system performance, cost efficiency, automated testing, and developer experience. You understand when a lightweight query or a simple, well-designed service beats introducing a complex, expensive distributed computing cluster. You choose simple, maintainable systems over over-engineered platforms.

What You’ll Do:

• Design, build, and maintain data processing pipelines using modern software engineering practices.
• Collaborate with product and backend engineers to design operational database schemas, analytical models, and efficient storage specs optimized for read/write patterns.
• Write clean, modular, and well-tested code (SQL, Python, TypeScript or similar).
• Audit slow queries, optimize storage formats (e.g., Parquet, Iceberg), tune database indexes, and keep infrastructure compute and storage costs lean and predictable.
• Enforce CI/CD and version control across all data systems.
• Set up robust automated data testing, alerting, and monitoring to catch system anomalies and data quality regressions before production impact.

What We’re Looking For:

• 5+ years of professional experience building software in production
• Deep understanding of relational database internals (Azure SQL or similar), indexing strategies, and query execution plans, alongside familiarity with column-store formats or distributed storage.
• Familiarity with serverless streaming ingestion platforms such as Cloudflare Pipelines — streams, SQL transforms, and Iceberg/Parquet sinks to R2.
• Fluency with AI-assisted development tooling (e.g., Claude Code, Cursor, Copilot) and a track record of using it to ship production code faster without sacrificing quality.
• Practical experience building AI/LLM-backed use cases on top of data systems — retrieval pipelines, embeddings and vector storage, evaluation harnesses, or agentic workflows against internal data.

Nice to Have:

• Exposure to open-source product analytics and event-tracking platforms (e.g., PostHog).
• Familiarity with lightweight analytical processing engines (e.g., DuckDB).
• Experience provisioning data infrastructure via code (e.g., Terraform).
• Experience with cloud data warehouse and lakehouse platforms (e.g., Snowflake, Databricks).

This position description may not describe all duties, responsibilities, and skills associated with this position. It is intended to portray the major aspects of the job. Other duties or skills may be required.