Data Warehouse Analytics Engineer

Salary

The gross annualized salary range for this role is $120,000 - $130,000

As an engineer at Amplify, you will join a talented team tackling the toughest problems in education with the best ideas in technology – including user experience, APIs and services, data analysis, and deployment pipelines. You’ll play an active role in imagining and improving product design and the classroom experience.

What You’ll Do

Our data analytics teams ingest, transform, model, and aggregate the data that empowers teams across Amplify and our customers to make sense of and tell stories with their data. You’ll be working with data scientists, data analysts, and data engineers to provide clean, accurate, reliable models and metrics for our products and internal business partners.

Impress the toughest customers around – students – by building the data models which drive the design cycle for fun and compelling apps, helping them understand their students by building modular data models
Help school administrators build great schools by: (1) respecting privacy and ensuring security while offering useful insights by making inquisitive choices in tech stack, database design, masking policies, and encryption; (2) helping school principals understand how teachers are teaching and how students are learning by architecting data warehouse schemas and SQL transforms with just the right CTEs, window functions, and pivots; and (3) analyzing performance and squashing tricky bugs using tools like Snowflake, Airflow, DBT, SQL, Python, Looker, and Datadog
Make life better for passionate, Marketing and Sales teams by building cohesive, common data marts from sources like Salesforce and HubSpot
Learn every day by: (1) immersing oneself in agile rituals and leveraging our infrastructure; (2) leading collaboration, pull request-ing, and mentoring on a multi-functional team; (3) participating in cross-team share-outs, brownbags, and workshop series, and (4) becoming an expert in the data models and standards within Amplify and the educational industry in order to deliver quality and consistent solutions

Example Projects You Might Work On

Building well-tested and documented ELT data pipelines for both full and delta extraction
Engineer novel dataset which expresses a students progress and performance through an adaptive learning experience which allows for flexible comparison across students and deep analysis of individual students.
Work with data science to measure the impact of design changes to an administrator reporting application.
Improve the build and query efficiency of data models constructed from a large volume of event stream data.
Contributing to leading industry data standards, such as Caliper Analytics or xAPI
Crafting slowly changing dimensional models that take into account the nuances of K-12 education such as School Year changes and students moving schools or classes.

Basic Requirements of the Data Warehouse Analytics Engineer:

BS in Computer Science, Data Science, or equivalent
2+ years of professional software development or data engineering experience
Strong CS and data engineering fundamentals
Proven fluency in SQL and its use in code-based ETL frameworks such as dbt
Understanding of ETL/ELT pipelines, analytical data modeling, aggregations, and metrics
Knowledge of Data Warehousing design, tooling, and support
Strong communication skills in writing and conversation

Preferred Requirements of the Data Warehouse Analytics Engineer:

Fluency in a development language such as Python
Familiarity with metadata management tools
Experience building dashboards, reports, models in business intelligence tools such as Tableau or Looker
Experience with tools we use every day:
Storage: Snowflake, AWS Storage Services (S3, RDS, Glacier, DynamoDB)
ETL/BI: Airflow, dbt, Matillion, Looker, Tableau
Cloud Infrastructure: AWS Kinesis, Lambda, API Gateway, Terraform
Experience with tools we don’t use, but should, Proven passion and talent for teaching fellow engineers and non-engineers
Proven passion for building and learning: open source contributions, pet projects, self-education, Stack Overflow
Experience in education or ed-tech

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