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Staff Analytics Engineer
Company: Amplify
Employment Type: Full-Time
Location: 100% Remote – US
Compensation: $175,000 – $185,000 (USD, plus annual discretionary bonus)
Category: Data/AI/Analytics
Close date or Apply by date: Not disclosed
WHY THIS ROLE MADE THE CUT
Fully remote (no hybrid or office requirements)
Full-Time role
Job is listed on company site with a direct link to apply
Legitimate, established company
Posted within the last 14 days OR includes a clear deadline to apply
Clear salary range is disclosed
ROLE SNAPSHOT
Amplify is hiring a Staff Analytics Engineer to work with data scientists, data analysts, data engineers, and software engineers transforming, modeling, and aggregating data that empowers customers to make sense of and tell stories with their data. You'll architect data warehouse schemas and SQL transforms using CTEs, window functions, and pivots, create data solutions using tools like Snowflake, Airflow, DBT, SQL, Python, and Cube.dev, build well-tested and documented ELT data pipelines for full and incremental dbt models, engineer novel datasets expressing student progress and performance through adaptive learning experiences, and craft slowly changing dimensional models accounting for K-12 education nuances like school year changes.
Experience required: BS in Computer Science, Data Science, or equivalent experience, 8+ years professional software development or data engineering experience, 5+ years in computer, data, and analytics engineering, expertise in SQL and code-based ETL frameworks (preferably dbt), expertise in ETL/ELT pipelines, analytical data modeling, and aggregations, and expertise in dbt, git, and analytical modeling architectures including Kimball design.
KEY WORDS TO INCLUDE IN YOUR RESUME/COVER LETTER IF YOU APPLY:
Analytics engineering and data modeling
SQL and ETL/ELT pipelines
dbt (data build tool)
Snowflake and data warehousing
Kimball dimensional modeling
Airflow and Python
Git and CI/CD processes
Cube.dev, Looker, or Tableau (preferred)
Education or EdTech experience (preferred)
Technical leadership and mentoring
Keywords are suggested based on the language used in the employer's job description to help applicants align with automated screening systems.
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