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Senior Data Engineer

Posted 2024-10-17

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πŸ’Ž Seniority level: Senior, 5+ years experience

πŸ“ Location: USA, CAN, MEX

πŸ” Industry: Transportation technology

🏒 Company: Fleetio

πŸ—£οΈ Languages: English

⏳ Experience: 5+ years experience

πŸͺ„ Skills: AWSProject ManagementPythonSQLBusiness IntelligenceDesign PatternsKafkaSnowflakeTableauData engineeringServerlessCommunication SkillsCI/CD

Requirements:
  • 5+ years experience working in a data engineering or data-focused software engineering role.
  • Experience transforming raw data into clean models using standard tools of the modern data stack.
  • Deep understanding of ELT and data modeling concepts.
  • Experience with streaming data and pipelines (Kafka or Kinesis).
  • Proficiency in Python with a proven track record of delivering production-ready Python applications.
  • Experience in designing, building, and administering modern data pipelines and data warehouses.
  • Experience with dbt.
  • Familiarity with semantic layers like Cube or MetricFlow.
  • Experience with Snowflake, BigQuery, or Redshift.
  • Knowledge of version control tools such as GitHub or GitLab.
  • Experience with ELT tools like Stitch or Fivetran.
  • Experience with orchestration tools such as Prefect or Dagster.
  • Knowledge of CI/CD and IaaC tooling such as GitHub Actions and Terraform.
  • Experience with business intelligence solutions (Metabase, Looker, Tableau, Periscope, Mode).
  • Familiarity with serverless cloud functions (AWS Lambda, Google Cloud Functions, etc.).
  • Excellent communication and project management skills with a customer service-focused mindset.
Responsibilities:
  • Enable and scale self-serve analytics for all Fleetio team members by modeling data and metrics via tools like dbt.
  • Develop data destinations, custom integrations, and maintain open source packages for customer data integration.
  • Maintain and develop custom data pipelines from operational source systems for both streaming and batch sources.
  • Work on the development of internal data infrastructure, improving data hygiene and integrity through ELT pipeline monitoring.
  • Architect, design, and implement core components of data platform including data observability and data science products.
  • Develop and maintain streaming data pipelines from various databases and sources.
  • Collaborate across the company to tailor data needs and ensure data is appropriately modeled and available.
  • Document best practices and coach others on data modeling and SQL query optimization, managing roles, permissions, and deprecated projects.
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