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# Sr. Analytics Engineer/ Data Scientist

- **Company:** Llc Laurel
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $205,000 - $249,000
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, BigQuery, D3.Js, Data Infrastructure, Extract Transform Load (ETL), Data Visualization, Data Warehousing, Software Design Patterns, DevOps, Python (Programming Language), Machine Learning, Productivity Software, Power BI, SQL Databases, Tableau (Software), Web Analytics, Web Applications, Google Cloud, Cloud Platform System, Sql Optimization, Snowflake, Kubernetes, Information Technology, Data Analytics, Terraform, Data Pipelines, Amazon Redshift
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/sr-analytics-engineer-data-scientist-san-francisco-ca--2c7dc40c-c8a9-456d-80f1-b53330335314

## About the Role

Data Science, Analysis Skills, Accounting Standards and Regulations, Artificial Intelligence (AI), Professional Services, Machine Learning, Profit & Loss, Intellectual Property (IP), Product Development, Product Engineering, * Education: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

 * Experience: 3+ years of professional experience as a Data Scientist. Ideal candidates will be comfortable working with large-scale data systems.
 * Technical Proficiency:
 + Advanced SQL and Python
 + Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster).
 + Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift).
 + Familiarity with data modeling, warehousing principles, and BI tools (e.g., Thoughtspot, PowerBI, Tableau).
 * Experience With:
 + Cloud platform expertise (AWS, GCP, Azure).
 + Knowledge of dbt, Kubernetes, and Terraform.
 + Exposure to CI/CD pipelines and DevOps practices.
 * Soft Skills:
 + Strong problem-solving and communication skills.
 + Ability to work in a fast-paced startup environment and manage multiple priorities.

Nice to haves

 * Experience with knowledge worker productivity tools
 * Familiarity with modern data visualization libraries (e.g., D3.js)
 * ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features

## Description

As a Senior Analytics Engineer/Data Scientist at Laurel, you'll turn product and business data into clear, trustworthy insights leaders can act on. You'll own the analytics lifecycle-from ingestion and modeling to BI visualization and decision enablement. You'll deliver self-serve insights using SQL/Python and embedded BI (e.g., ThoughtSpot). You will also help define the design patterns and data infrastructure to scale.

We're especially interested in candidates who thrive in early-stage environments and pair analytical rigor with clear storytelling. You'll partner closely with our CX team to quantify and communicate Laurel's ROI, and you'll join customer-facing presentations. You will be able to translate complex methods for non-technical leaders while going deep with technical stakeholders when needed.

What you will do

 1. Build analyses & automation (SQL/Python)
 + Run recurring ROI analyses (Business Impact Reports). Write performant SQL and pandas; productionize repeatable jobs (scheduling, alerts, anomaly checks) with orchestration (e.g., Airflow).
 2. Define metrics & model the data
 + Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth.
 3. Partner with CX on customer ROI
 + Quantify and communicate Laurel's impact, prepare exec-ready materials, and join customer-facing presentations-translating for both non-technical and technical stakeholders.
 4. Ship dashboards & actionable insights
 + Deliver customer-facing ThoughtSpot dashboards and turn findings into concise actionable insights
 5. Raise data quality & instrumentation
 + Add validation tests and monitoring, triage data issues quickly, and collaborate with Product/Engineering to improve data quality.
 6. Data Platform Development
 + Design, build, and maintain Laurel's Analytics Data Warehouseas the single source of truth for analytics and reporting needs.
 + Create scalable ETL pipelines to ingest, process, and organize data from diverse sources (PMS, Web Analytics, WebApp).
 + Deploy and maintain Business Intelligence tools to provide analytics and reporting capabilities.