Senior Analytics Engineer

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💎 Seniority level: Senior, 4+ years (Analytics Engineer), 6+ years (data space)

📍 Location: United States

💸 Salary: 150000.0 - 200000.0 USD per year

🔍 Industry: Energy

🏢 Company: Arcadia👥 501-1000💰 $125,000,000 over 2 years agoDatabase CleanTech Renewable Energy Clean Energy Software

🗣️ Languages: English

⏳ Experience: 4+ years (Analytics Engineer), 6+ years (data space)

🪄 Skills: AWSPythonSQLData AnalysisETLGitSnowflakeData engineeringData StructuresCommunication SkillsAnalytical SkillsCollaborationData visualizationData modelingData management

Requirements:

4+ years as an Analytics Engineer or equivalent role; experience with dbt is strongly preferred
6+ years, cumulatively, in the data space (data engineering, data science, analytics, or similar)
Expert-level understanding of conceptual data modeling and data mart design
An understanding of data structures and/or database design plus deep experience with SQL and Python
Experience building data pipelines and database management including Snowflake or similar
Ability to bring a customer-oriented and empathetic approach to understanding how data is used to drive the business
Experience in technical leadership or mentorship
Strong communication and collaboration skills
Proven ability to solve complex problems in a dynamic and evolving environment

Responsibilities:

Transform, test, deploy, and document data to deliver clean and trustworthy data for analysis to end-users
Collaborate with subject matter experts, engineers, and product managers to identify the most elegant and effective data structures to understand our constantly growing and evolving business
Help bring engineering best practices (reliability, modularity, test coverage, documentation) to our DAG and to our Data team generally
Collaborate with data engineers to build robust, tested, scalable ELT pipelines.
Data modeling: model raw data into clean, tested, and reusable datasets to represent our key business data concepts. Define the rules and requirements for the formats and attributes of data
Data transformation: build our data lakehouse by transforming raw data into meaningful, useful data elements through joining, filtering, and aggregating source dataData documentation: create and maintain data documentation including data definitions and understandable data descriptions to enable broad-scale understanding of the use of data
Employ software engineering best practices to write code and coach analysts and data scientists to do the same

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