- San Francisco
- Date Posted
- Jun. 7, 2021
- Data Science
At Transform, we are building a metrics repository that enables businesses to capture metric definitions in a standardized, well-formatted, and organized way to streamline analysis and enable decision-making with confidence and speed. We bridge the gap between those who know the data and those who need the data by creating a trusted metrics repository with an accessible user interface and a broad set of connectors to seamlessly pipe data to downstream systems.
We are backed by Index, Redpoint, Fathom, and Work Life Ventures and have years of experience working on data at Airbnb, Facebook, Coinbase, BlackRock, and Instagram. We’ve dedicated our careers to improving data infrastructure, from warehouses to machine learning platforms. We saw at Airbnb that a metrics repository improves both the speed and trustworthiness of data across all tools at a company.
As a data engineer at Transform, you’ll work on Transform’s core product, Metrics Query Language (MQL), a metrics framework for defining, efficiently computing, storing and serving metrics to applications. You’ll contribute to a wide range of initiatives, flexing abilities from data science & data engineering, to backend systems & infrastructure engineering in order to deliver products. The ideal candidate will be proactive and opinionated about the future of data engineering systems and architecture patterns. This role is self-directed with the opportunity to grow into a leadership role.
What you will do at Transform
- Develop and expand functionality of Transform’s core Metrics Framework & APIs
- Increase performance, reliability, and visibility across multiple companies & infrastructure patterns
- Work with customers to identify gaps in our product, pain points in their data systems and identify novel solutions to their challenges
- Learn from industry leaders; build a world-class team, mentor new engineers, and develop our diverse culture
What you will need to be successful
- Experience designing, implementing, and maintaining data engineering tool and systems
- Natively speak core data engineering concepts like measures, dimensions, and partitions
- Familiarity with large scale data processing engines & SQL-like languages
- Ability to independently build lasting pieces of software, with little operational overhead
- High-level of familiarity with Python or other data-centric languages
Nice to haves
- Technical leadership and/or people leadership experience
- Familiarity with gathering feedback and working with customers of data or tools.
- Experience or familiarity with the following software/tools:
- OLAP databases: Snowflake, Redshift, BigQuery, Hadoop
- Workflow management tools: Airflow, Dagster, AWS Glue, etc.
- Cloud services: AWS, GCP, Azure, etc.