Sr. Data Platform Engineer

Accountabilities: Build and optimize data pipelines: Design, implement, and maintain scalable batch and streaming pipelines that efficiently process large volumes of diverse data. Develop data processing systems: Build extensible data infrastructure that supports the addition, maintenance, and scaling of pipelines as business and product needs evolve. Process and model complex datasets: Extract, analyze, transform, and model structured and unstructured data to generate actionable insights and support intelligent applications. Ensure production reliability: Develop and maintain data pipelines with strong reliability, scalability, fault tolerance, and observability, ensuring consistent performance in production environments. Apply strong software engineering practices: Design testable, maintainable, and reusable software components while following sound development lifecycle practices and engineering standards. Contribute to data strategy: Help shape the data platform's technical direction, architecture, and long-term vision, making informed decisions about infrastructure and implementation approaches. Apply machine learning techniques: Use methods such as anomaly detection, clustering, regression, classification, and summarization to extract meaningful value from complex datasets. Leverage AI-assisted development: Use tools such as Cursor and other AI-powered coding assistants to accelerate development, scaffold components and APIs, refactor legacy code, improve documentation, and prototype solutions. Improve engineering tooling and practices: Stay informed about emerging data engineering technologies and incorporate relevant tools, frameworks, and best practices into the platform. Collaborate and take ownership: Work closely with product and engineering colleagues, communicate technical ideas clearly, take initiative in solving complex problems, and contribute to continuous improvement across teams. Requirements Professional experience: At least 4 years of experience in Data Engineering and at least 4 years of hands-on Python development. Production data engineering: Demonstrated experience building, maintaining, and operating production-grade data pipelines with a focus on reliability, scalability, fault tolerance, and observability. Core data engineering knowledge: Solid understanding of ETL/ELT processes, batch and streaming architectures, data warehouses, data lakes, and distributed data processing. Python and data processing: Strong Python programming skills and familiarity with libraries and frameworks such as Pandas and PySpark. Software engineering fundamentals: Strong knowledge of the software development lifecycle (SDLC), testing, maintainability, and other established software engineering practices. Infrastructure as code: Familiarity with infrastructure-as-code principles and tools for managing and deploying infrastructure. Technical stack familiarity: Experience with some of the following technologies is expected: Languages and data tools: Python, PySpark, Pandas, SQL, and PostgreSQL. Data orchestration and search: Airflow and Elasticsearch. Cloud and data platforms: AWS services such as S3, Batch, Athena, RDS, DynamoDB, Glue, ECS, and Amazon Neptune; Databricks. Development and infrastructure tools: Docker, CircleCI, GitHub, and Terraform. AI-assisted coding expertise: Familiarity with AI-powered coding tools such as Cursor, GitHub Copilot, or Codex, with practical experience using AI-enabled development environments. Effective AI development workflows: Demonstrated ability to use AI coding assistants to scaffold components and APIs, refactor existing code efficiently, reduce context switching, accelerate documentation, and prototype with rapid feedback. Analytical problem-solving: Strong critical thinking and problem-solving abilities, with the capacity to investigate complex technical challenges and develop practical solutions. Communication and collaboration: Ability to explain technical concepts clearly, exchange ideas effectively, and collaborate with colleagues across teams. Ownership and initiative: A self-directed approach to work, a willingness to take responsibility for technical outcomes, and the curiosity to explore new technologies and continuously improve. Learning mindset: Genuine interest in emerging data engineering practices, AI-assisted development, and evolving technologies that can improve platform capabilities. Benefits Competitive salary: Starting at $170,000 USD per year, with the potential for a higher offer based on expertise, seniority, location, cost of living, taxes, and market experience. Equity participation: Equity in a well-funded, fast-growing technology company. Flexible time off: Unlimited vacation time, allowing employees to take time off as needed. Healthcare coverage: 99% of health, dental, and vision insurance premiums covered for employees and their dependents. Retirement savings: Access to a 401(k) plan through Empower, with investment options. Remote work: Fully remote position within the United States. Growth and technical ownership: The opportunity to influence technical direction, work with modern data and AI technologies, and contribute to the development of a scalable data platform. Inclusive workplace: A commitment to equal opportunity, diversity, equity, and belonging, with encouragement for candidates to apply even if they do not meet every listed qualification.