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Analytics Engineer

Posted 17 days agoViewed

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💎 Seniority level: Senior, 5 - 10 years

📍 Location: United States, Canada

🔍 Industry: Data & Finance

🏢 Company: AirGarage👥 11-50💰 $1,000 4 months agoInternetTransportationOnline PortalsPaymentsParkingReal EstateSoftware

🗣️ Languages: English

⏳ Experience: 5 - 10 years

🪄 Skills: PythonSQLBashData AnalysisETLGitKubernetesSnowflakeAlgorithmsAPI testingData engineeringData StructuresREST APICI/CDData visualizationData modelingData analyticsData management

Requirements:
  • A degree in engineering, science, math, or a related analytical field.
  • 5 - 10 years of experience in Data Engineering or Analytics Engineering.
  • Experience with the modern data stack (Snowflake / dbt / Polytomic / Hex or equivalents).
  • Advanced fluency in SQL and Python (bonus for Snowpark) — knowing how to write intricate queries and build automated pipelines.
  • Prior experience with data modeling and writing performant queries in a data warehouse setting on large datasets.
  • Passion for data
  • Extreme attention to detail and nuance
  • Innate curiosity
  • Systems-thinker
  • A growth mindset
Responsibilities:
  • Partner with our founding team and company leadership to build out a best-in-class analytics system to give our partners insight into their parking lot and garage performance and super charge our internal organization.
  • Work with our engineering, data, sales, marketing, and product teams to understand the data needs of the business and produce pipelines, data marts and other data solutions that enable better decision-making.
  • Develop scalable code that transforms raw data in an automated and efficient manner to ensure curated and ready-to-analyze data is always available.
  • Identify new data sources we can leverage to improve the company’s performance in core business activities; this can take the form of collaborating with our engineering team to improve data logging, scraping data from the web, or using third-party datasets.
  • Conduct data analyses to assist partner teams in diagnosing issues, understanding opportunities, or measuring performance.
  • Troubleshoot and communicate data issues to internal stakeholders, including by building code tests to alert us when there is a problem in our data before it affects end users.
  • Play a critical role in helping define data team best practices, workflows, and documentation to ensure we are building a sustainable and scalable data function.
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