Senior Data Engineer

Minimum of 6 years of experience working in data engineering
Expertise both in using SQL and Python for data cleansing, transformation, modeling, pipelining, etc.
Proficient in working with other stakeholders and converting requirements into detailed technical specifications; owning and leading projects from inception to completion
Proficiency in working with high volume datasets in SQL-based warehouses such as BigQuery
Proficiency with parallelized python-based data processing frameworks such as Google Dataflow (Apache Beam), Apache Spark, etc.
Experience using ELT tools like Dataform or dbt
Professional experience maintaining data systems in GCP and AWS
Deep understanding of data modeling, access, storage, caching, replication, and optimization techniques
Experienced with orchestrating data pipelines and Kubernetes-based jobs with Apache Airflow
Understanding of the software development lifecycle and CI/CD
Monitoring and metrics-gathering (e.g. Datadog, NewRelic, Cloudwatch, etc)
Willingness to participate in a weekly on-call support rotation – currently the rotation is monthly
Proficiency with git and working collaboratively in a shared codebase
Excellent documentation skills
Self motivation and a deep sense of pride in your work
Passion for the outdoors
Comfort with ambiguity, and an instinct for moving quickly
Humility, empathy and open-mindedness – no egos

Work cross-functionally to ensure data scientists have access to clean, reliable, and secure data, the backbone for new algorithmic product features
Build, deploy, and orchestrate large-scale batch and stream data pipelines to transform and move data to/from our data warehouse and other systems
Deliver scalable, testable, maintainable, and high-quality code
Investigate, test-for, monitor, and alert on inconsistencies in our data, data systems, or processing costs
Create tools to improve data and model discoverability and documentation
Ensure data collection and storage adheres to GDPR and other privacy and legal compliance requirements
Uphold best data-quality standards and practices, promoting such knowledge throughout the organization
Deploy and build systems that enable machine learning and artificial intelligence product solutions
Mentoring others on best industry practices

Experience working in a multi-cloud environment
Experience with GIS, H3, or other mapping technologies
Experience with Amplitude
Experience with infrastructure-as-code, such as Terraform
Experience with machine learning frameworks and platforms such as VertexAI, SageMaker, MLFlow, or related frameworks