Senior Data Platform Engineer
New
J
JobgetherData engineering
Fully remote position within the United States.Full-TimeSenior
SalaryStarting 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.
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Job Details
- Experience
- At least 4 years of experience in Data Engineering and at least 4 years of hands-on Python development.
- Required Skills
- AWSPostgreSQLPythonSQLApache AirflowElasticSearchPandasTerraformDatabricksPySpark
Requirements
- At least 4 years of experience in Data Engineering.
- At least 4 years of hands-on Python development.
- Experience building, maintaining, and operating production-grade data pipelines.
- Knowledge of pipeline reliability, scalability, fault tolerance, and observability.
- Understanding of ETL/ELT, batch and streaming architectures, data warehouses, data lakes, and distributed data processing.
- Strong Python skills and familiarity with Pandas and PySpark.
- Knowledge of the software development lifecycle, testing, maintainability, and software engineering practices.
- Familiarity with infrastructure-as-code principles and tools.
- Experience with some of the listed technologies, including SQL, PostgreSQL, Airflow, Elasticsearch, AWS services, Databricks, Docker, CircleCI, GitHub, or Terraform.
- Familiarity with AI coding tools such as Cursor, GitHub Copilot, or Codex, and practical experience using AI-enabled development environments.
- Ability to use AI coding assistants to scaffold components and APIs, refactor code, accelerate documentation, and prototype with rapid feedback.
- Strong analytical problem-solving skills and ability to collaborate and explain technical concepts clearly.
Responsibilities
- Design, implement, and maintain scalable batch and streaming data pipelines.
- Build extensible data infrastructure that supports the addition, maintenance, and scaling of pipelines.
- Extract, analyze, transform, and model structured and unstructured datasets.
- Develop and maintain reliable, scalable, fault-tolerant, and observable production data pipelines.
- Design testable, maintainable, and reusable software components using sound engineering practices.
- Contribute to the data platform's technical direction, architecture, and long-term vision.
- Apply anomaly detection, clustering, regression, classification, and summarization to complex datasets.
- Use AI coding assistants to scaffold components and APIs, refactor code, improve documentation, and prototype solutions.
- Collaborate with product and engineering colleagues and contribute to continuous improvement.
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