Sr. Analytics Engineer/ Data Scientist

Laurel•San Francisco, CA

1d•$218,000 - $240,000•Hybrid

About The Position

As a Senior Analytics Engineer/Data Scientist at Laurel, you will turn product and business data into clear, trustworthy insights that leaders can act on. You will own the analytics lifecycle, from ingestion and modeling through BI visualization and decision enablement, and deliver self-serve insights using SQL, Python, and embedded BI tools such as ThoughtSpot. You will also help define design patterns and data infrastructure that can scale. The role partners closely with the Customer Experience team to quantify and communicate Laurel’s return on investment, and includes participation in customer-facing presentations. You will translate complex methods for non-technical leaders and collaborate in depth with technical stakeholders. You will work in an early-stage startup environment, combining analytical rigor with clear storytelling. You will build and maintain Laurel’s Analytics Data Warehouse as the single source of truth for analytics and reporting; create scalable ETL pipelines for data from sources such as PMS, web analytics, and the web application; and deploy and maintain BI tools. The role is hybrid in Laurel’s San Francisco office three days per week. Exceptionally qualified candidates elsewhere in the United States may be considered on a case-by-case basis. Visa sponsorship and relocation assistance are not available.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
At least 3 years of professional experience as a Data Scientist.
Comfort working with large-scale data systems.
Advanced SQL and Python proficiency.
Experience with data orchestration tools such as Airflow, Prefect, or Dagster.
Proficiency with modern data warehouses such as Snowflake, BigQuery, or Redshift.
Familiarity with data modeling, data warehousing principles, and BI tools such as ThoughtSpot, Power BI, or Tableau.
Cloud platform expertise in AWS, GCP, or Azure.
Strong problem-solving and communication skills.
Ability to work in a fast-paced startup environment and manage multiple priorities.

Nice To Haves

Knowledge of dbt, Kubernetes, and Terraform.
Exposure to CI/CD pipelines and DevOps practices.
Experience with knowledge worker productivity tools.
Familiarity with modern data visualization libraries such as D3.js.
Experience prototyping, building, and testing machine-learning models to validate hypotheses and generate product insights.

Responsibilities

Build analyses and automation using SQL and Python, including recurring Business Impact Reports and performant SQL and pandas workflows.
Productionize repeatable jobs with scheduling, alerts, and anomaly checks using orchestration tools such as Airflow.
Own metric definitions, including measures such as True Time versus Released, and create reusable SQL data models as the analytics source of truth.
Partner with the Customer Experience team to quantify and communicate Laurel’s impact, prepare executive-ready materials, and participate in customer-facing presentations.
Deliver customer-facing ThoughtSpot dashboards and turn findings into concise, actionable insights.
Add data validation tests and monitoring, triage data issues, and collaborate with Product and Engineering to improve data quality.
Design, build, and maintain Laurel’s Analytics Data Warehouse as the single source of truth for analytics and reporting.
Create scalable ETL pipelines to ingest, process, and organize data from sources including PMS, web analytics, and the web application.
Deploy and maintain Business Intelligence tools for analytics and reporting.
Prototype, build, and test machine-learning models to validate hypotheses and generate insights that guide Laurel product features.