Sr. Analytics Engineer/ Data Engineer

What you will do

• 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).
• 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.
• 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.
• Ship dashboards & actionable insights
• Deliver customer-facing ThoughtSpot dashboards and turn findings into concise actionable insights
• Raise data quality & instrumentation
• Add validation tests and monitoring, triage data issues quickly, and collaborate with Product/Engineering to improve data quality.
• Data Platform Development
• Design, build, and maintain Laurel’s Analytics Data Warehouse as 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.
• You will be a great fit if you have
• 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).
• Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction.
• 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
• Flexibility and Logistics
• Location: This role will be hybrid based in our San Francisco office, 3 days per week. We will consider exceptionally qualified candidates based in other US-locations on a case by case basis.
• Compensation: Competitive salary, generous equity, comprehensive medical/dental/vision coverage with covered premiums, 401(k), additional benefits including wellness/commuter/FSA stipends. For candidates based in San Francisco the compensation range for this role is $205,000-$249,000 USD. Final compensation amounts will be determined based on several factors including candidate experience, qualifications and expertise and may vary from the amounts listed.
• Visa Sponsorship: Unfortunately we are unable to provide Visa Sponsorship at this time.