Data Engineer

Industry: Retail
Company size: 11-50

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Job description

A US-based company that is building an AI-based SaaS platform that is built to eliminate friction between patients and clinics, is looking for a Data Engineer. The selected candidate will be tasked with troubleshooting and improving the performance of reporting and data solutions. The company uses automation and AI to provide innovative healthcare, integrating cutting-edge technology and process design to patient involvement and provider consultation. They have managed to raise more than $9.5mn during their Series A round of funding. This will be a full-time, long-term position that requires 4+ hours of overlap with the PST time zone.


Job Responsibilities:

  • Work on designing databases and data pipelines/ETL using the latest technologies and tools
  • Assist the development team to build operationally efficient analytic solutions
  • Define standards and methodologies for the Data Warehousing environment
  • Design and develop scalable data pipelines utilizing modern tools and technologies like AWS, Snowflake, Spark, and Kafka to induct data from various systems
  • Translate complicated business requirements to scalable technical solutions to meet data warehousing design requirements
  • Build easy to scale data pipelines and ETL apps to support business operations in areas like advertising, content, and finance/accounting
  • Assist with deciphering data migration issues and improving system performance
  • Work closely alongside product management, technical program management, operations, and other engineers

Job Requirements:

  • Bachelor’s/Master’s degree or Ph.D. in Engineering, Computer Science (or equivalent experience)
  • At least 3+ years of relevant experience as a Data Engineer
  • At least 2 years of experience working with Python
  • Should possess a deep understanding of Django with the ability to build APIs
  • Thorough knowledge of building scalable data systems and data-driven products working while working with cross-functional teams
  • Must be able to build data pipelines and ETL applications with large data sets
  • Must have a thorough knowledge of developing REST APIs for back-end services
  • Familiarity of implementing, testing, debugging, and deploying data pipelines using tools like Prefect, Airflow, Glue, Kafka, Serverless (Lambda, Kinesis, SQS, SNS), Fivetran, or Stitch Data/Singer
  • Familiarity with Cloud data warehousing technologies like Redshift, BigQuery, Spark, Snowflake, Presto, Athena, and S3
  • Understanding of SQL DB administration (PostgreSQL, MS SQL, etc.)
  • Nice to have some DevOps experience
  • Knowledge complex, distributed, and microservice web architectures are preferable
  • Deep understanding of analytics needs and proactive-ness to build generic solutions for improving  efficiency

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