Sr. Analytics Engineer/ Data Scientist

present of AI; San Francisco (CA); Sur place; USD 120 000 - 180 000; Plein temps

Résumé du poste

Laurel seeks a data-focused analytics engineer to build and run the data warehouse, write feeding pipelines, and define metrics that drive decision making. The role blends data science with analytics engineering and requires clear storytelling to executives and technical teams.

You will turn raw timekeeping data into dashboards and ROI reports in a small, fast-moving company. 3+ years of hands-on data work, strong SQL and Python, and startup adaptability are essential.

Qualifications

• Three plus years of hands-on data work.
• Strong SQL and Python skills.
• Ability to explain findings clearly to executives and to technical teams.
• Comfort in a small, fast-moving company.

Responsabilités

• Build and run the data warehouse and the pipelines that feed it.
• Define key metrics and turn results into dashboards and ROI reports.
• Explain insights clearly to executives and technical teams.
• Collaborate closely with product and engineering to improve data quality and reliability.

Connaissances

Advanced SQL

Python

Dashboards

Data quality testing

Data modeling

Data warehouse

Data pipelines

Metrics definition

Data governance

Description du poste

This role mixes data science with analytics engineering. The person builds and runs the data warehouse, writes the pipelines that feed it, defines the key metrics, and turns the results into customer-facing dashboards and ROI reports. A strong candidate has about three years or more of hands-on data work, is very good at SQL and Python, and can explain findings clearly to executives as well as to technical teams. Comfort in a small, fast-moving company matters too.

How it fits the AI era

Laurel's product already uses machine learning to capture work time, so much of the raw data comes from AI. Tools can help draft queries, flag odd data and speed up routine reports, which leaves more room for judgment. What stays human is deciding which metrics are trustworthy, spotting when numbers do not match reality, and telling a convincing story to a client who is weighing the value of the product.

How it builds AI experience

The work sits right next to an AI product, so the person learns how large volumes of activity data are cleaned, defined and measured before they reach customers. The listed nice to have of prototyping and testing ML models offers a path into product-level modeling. To build this experience beforehand, a candidate could practice measuring the business impact of an automated system using time or activity data.

Worth showing

• Advanced SQL and Python
• Warehouse and pipeline design
• Metric definition and data modeling
• Dashboards for non-technical leaders
• Data quality testing and monitoring

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Laurel

Laurel is an AI timekeeping and work intelligence platform that automatically records billable work at the source, per the company.