Data Engineer
Builds and optimizes scalable data pipelines, storage, and OLAP databases for ML training, analytics, and product features. Requires 5+ years in data engineering, proficiency in Python/SQL/cloud platforms, and distributed systems experience.
About the job
What You’ll Do
- Build and maintain scalable data services, pipelines and storage solutions for the feedback of unstructured application data for ML training and evaluation purposes.
- Build and manage OLAP databases, ELTs and general data tooling for analytics, business decisions and products features.
- Work closely with a team of frontend and backend engineers, product managers, and analysts.
- Optimize data infrastructure to enhance the throughput, latency and reliability of the data system.
- Investigate and correct issues identified through data operations monitors, tools, and reports.
- Designs data integrations and data quality framework.
What You’ll Bring
- 5+ years of experience in Data Engineering or Backend Engineering with a focus on data systems.
- Proficient in at least one general purpose programming language (e.g., Python, Java, Scala) and SQL (any variant)
- Proficiency with at least one modern cloud provider (GCP, AWS, Azure) and accompanying data services
- Experience in building systems that manage the ingest, transformation, and management of both structured and unstructured data types
- Deep knowledge of modern data infrastructure best practices
- Experience with distributed systems and different distributed processing frameworks
- Experience with Terraform, Kubernetes, and containerization technologies.
- Familiarity with the deploying ML models at scale a bonus
- Experience in building data products that are well-modeled, documented and easy to understand and maintain.
- Ability to prioritize amidst changing priorities in a fast moving environment
Skills
Python, Java, Scala, SQL, GCP, AWS, Azure, Terraform, Kubernetes, Olap, ELT, Distributed Systems
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