Mozart Data (S20)
The easiest way to set up a modern data stack

Senior Backend Engineer

Remote / Remote
Full-time
3+ years
About Mozart Data

We're bringing the power of a large data team to everyone, building an easy-to-use application for ingesting data from any source into a managed data-warehouse, exploring and annotating that data, scheduling SQL transformations, and exporting data to BI tools or other systems.

About the role

Skills: Django, Python, React, Google Cloud, SQL

We're bringing the power of a large data team to everyone, building an easy-to-use application for ingesting data from any source into a managed data warehouse, exploring and annotating that data, scheduling SQL transformations, and exporting data to BI tools or other systems.

This role includes owning systems and features of our data-platform - primarily working collaboratively with the rest of our product & engineering team, building APIs and models to extend our Django backend, and building synchronous and asynchronous systems that interact with our multi-tenant Snowflake data-warehouse and third-party APIs like Fivetran, Salesforce, and Google Sheets.

If interested, this role could include some devops as well - we use Google Cloud, including AppEngine, CloudSQL, MemoryStore, and Cloud Tasks.

Technology

Our tech stack is primarily Python/Django + React on GCP, and we manage a Snowflake data warehouse for each of our customers.

Some upcoming projects you could work on:

  • Integrating asynchronous-django to our django API server, to support long-running queries that can't be handled in a normal request/response cycle.

  • Building systems to export data from our databases into numerous different external systems - eg: moving customer data into Salesforce, or Google Sheets, or Zendesk.

  • Building internal tooling to view the status and act on the many data pipelines running in our systems - both ETL pipelines and data transformations.

  • Optimizing the ordering the SQL data transformations. We'd like our users to not have to think about DAGs and data dependencies - we'd like to do that optimally for them automatically.

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