Última atualização: 4 de Julho de 2024

Data Engineering

💬 Inglês✈️ Vaga internacional🌍 100% Remoto

Via Shopify

Sobre

Example day-to-day responsibilities include:

  • Working with business partners to understand business and product objectives, and identify the data needed to support them
  • Working with engineers to understand and influence how data is produced, while influencing product and program decisions with data
  • Designing, building, implementing, and documenting data models
  • Writing data transformations using dbt or Spark
  • Shipping data pipelines including real-time streaming and batch processing
  • Optimizing data transformation pipelines to increase freshness or reduce computational time/cost
  • Collaborating with other data engineers on tooling for automated tasks related to consuming, validating raw/modeled data, updating modeled data
  • Collaborating with closely tied disciplines (Engineering, Data Science) to establish best practices and support the priorities of the Data Engineering craft
  • Building production-quality dashboards and scalable data products
  • Debating whether leading or trailing commas are better
  • Fixing jobs that are broken in production (wrong data types returned, granularity not as expected, resourcing issues)

In a manager role, you’d also be:

  • Providing leadership within your team — both from a technical and people standpoint — to support the mission of the Data Engineering discipline
  • Deploying your team towards data engineering priorities, achieving measurable impact and outcomes.
  • Defining and executing on your team’s deliverables
  • Leading all aspects of performance management, including compensation, performance improvement plans, promotions, and separations

You might be great in this role if you are:

  • Expert in data architecture and modeling concepts, and able to influence others to up-level the craft of Data Engineering 
  • Experience creating alignment with senior stakeholders on technical direction for ambiguous problem areas
  • Commercial experience in Data Engineering and/or Analytics Engineering, ​​​​​​and building scalable data warehouses
  • Dimensional Modeling (Star Schema, Kimball, Inmon)
  • Advanced SQL skills (ease with window functions, defining UDFs)
  • Exposure to Data Engineering tooling: ingesting, testing transformations, lineage, orchestration, publishing data, metric layers
  • Hands-on experience implementing real-time and batch data pipelines with tight SLOs and complex transformation requirements
  • Fantastic collaboration and communication skills, demonstrated by successful large-scale projects spanning multiple teams
  • High proficiency for product analysis, dashboarding, and reporting

For a staff or senior staff role, you’d also have:

  • Proven ability to provide leadership and guidance beyond one-on-one mentorship, and the ability to improve data engineering practices across teams

Outras Informações

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