Key skills
SQLPythonETL/ELT pipelinesData WarehousingAWSData LakesSnowflakeTerraformMLOps toolingOpen source contributionsExperience in educationcommunication skillsAIMachine LearningPyTorchTensorFlowMLOpsETLELTData EngineeringData LakeAnalyticsLookerBIAirflowdbtFivetranAzureGCPS3DynamoDBRDSSageMakerVertex AIDatadogAgileMentoringCommunicationCollaboration
About this role
Amplify is a pioneer in K–12 education leading the way in next-generation curriculum and assessment. As a Data Engineer, you will build and maintain data infrastructure, enabling teams to utilize data effectively and improve educational outcomes.
Responsibilities:
- Helping teams create fun, compelling apps by using millions of data points
- Helping teachers understand their students by building reusable data pipelines
- Implementing and managing data storage solutions (e.g., data lakes, databases)
- Building and maintaining ETL & machine learning pipelines on AWS
- Collaborating with data scientists and analysts to facilitate data access and utilization
- Ensuring data privacy and compliance with relevant regulations
- Analyzing and improving performance and squashing tricky bugs using tools like: Snowflake, Airflow, dbt, SQL, Python, Looker, Terraform, and Datadog
- Immersing oneself in agile rituals and using our infrastructure
- Leading collaboration, pull request-ing, and mentoring on a multi-functional team
- Participating in cross-team share-outs, brown bags, and workshop series
- Becoming an expert in the data models and standards within Amplify and the educational industry in order to deliver quality and consistent solutions
- Building well-tested and optimized ETL data pipelines for both full and delta extraction
- Collaborating with data analysts and learning scientists to gather, design, and implement ETL and Data Warehousing requirements
- Contributing to leading industry data standards (Caliper Analytics or xAPI), communities or open source projects (dbt)
- Build and support a machine learning model pipeline from development through production deployment
Requirements:
- BS in Computer Science, Data Science, or equivalent
- 2+ years of professional software development, site reliability, devops, or data engineering experience
- Strong CS and data engineering fundamentals
- Proven fluency in SQL and a development language such as Python
- Understanding of ETL/ELT pipelines and Data Warehousing design, tooling, and support
- Understanding of different data formatting (JSON, CSV, XML) and data storage techniques (3NF, EAV Model, Star Schema, Data Lake)
- Strong communication skills in writing and conversation
- Experience with MLOps tooling such AWS Sagemaker, GCP Vertex AI and frameworks like Pytorch or Tensorflow
- Experience with tools we use every day: Storage: Snowflake, AWS Storage Services (S3, RDS, Glacier, DynamoDB)
- ETL/BI: Cube, dbt, Fivetran, Looker
- Cloud Infrastructure: AWS/GCP/Azure, 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