Senior Software Engineer, Data

About the role

In this role, you will be responsible for designing, building, and maintaining the data infrastructure that powers our data-driven products and services.

Data engineering responsibilities

• Design and build robust data pipelines to ingest, transform, and load data from a variety of internal and external sources.
• Develop and maintain large, high quality datasets that power machine learning models and analytical products.
• Build scalable data storage, processing, and serving infrastructure on cloud platforms.
• Develop tooling and services for data labeling, data review, and dataset curation at scale.
• Find and evaluate new external data sources - manage relationships with data partners and vendors.
• Contribute to CI/CD practices and engineering standards across the data platform.

Data science responsibilities

• Partner with ML engineers and researchers to design feature pipelines and experiment infrastructure.
• Apply statistical and analytical techniques to assess dataset quality, identify gaps, and surface insights.
• Develop efficient algorithms and data models to curate data and maintain high quality and consistency.
• Design and evaluate metrics to measure dataset health and model readiness.
• Translate ambiguous research questions into well defined data problems with measurable outcomes.

Minimum qualifications

• 7+ years of software development experience.
• 5+ years of hands on experience managing and engineering data at scale.
• Proficiency in Python, TypeScript and SQL.
• Experience with cloud data services and storage technologies (e.g., S3, Redshift, BigQuery, Snowflake).
• Familiarity with statistical analysis and core data science concepts (feature engineering, data distributions, model evaluation etc).
• Degree in Computer Science, Statistics, a related technical field, or equivalent experience.
• Strong problem solving skills with the ability to work independently and drive projects end-to-end.

Preferred skills and experience

• Experience building large datasets for machine learning training and evaluation.
• Proficiency with data science libraries (pandas, NumPy etc.).
• AWS experience, including CDK or similar managed services.
• Experience with workflow orchestration tools (Airflow, Prefect etc).
• Experience with modern CI/CD workflows
• Experience partnering with external organizations or managing 3rd party data vendors.