1 день назад
Senior Analytics Engineer/Data Engineer (AI)
218 000 - 240 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Senior Analytics Engineer/Data Engineer (AI): Building Laurel’s analytics warehouse, ETL pipelines, BI dashboards, and machine learning prototypes to turn product and business data into actionable insights with an accent on data modeling, automation, and customer ROI analysis. Focus on designing scalable data infrastructure, improving data quality, and translating complex analytical findings for technical and non-technical stakeholders.
Location: Hybrid in the San Francisco office three days per week; exceptionally qualified candidates in other US locations may be considered case by case.
Salary: $218,000–$240,000 base compensation annually, plus equity. A separate San Francisco range of $205,000–$249,000 is also stated.
Company
is an AI Time platform for professional services firms that uses machine learning to capture, analyze, and optimize work time.
What you will do
- Build recurring ROI analyses and productionize SQL/Python data jobs with scheduling, alerts, and anomaly checks.
- Define business metrics and create reusable SQL data models as the analytics source of truth.
- Design, build, and maintain the analytics data warehouse and scalable ETL pipelines for diverse data sources.
- Deliver customer-facing ThoughtSpot dashboards and communicate ROI insights with the CX team and customers.
- Improve data quality through validation tests, monitoring, instrumentation, and issue triage.
- Prototype and apply machine learning models, including classification, clustering, regression, and NLP, to product and business problems.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 3+ years of professional experience as a Data Scientist and comfort working with large-scale data systems.
- Advanced SQL and Python skills, with experience in data modeling, warehousing, and BI tools.
- Experience with orchestration tools such as Airflow, Prefect, or Dagster and warehouses such as Snowflake, BigQuery, or Redshift.
- Ability to build and prototype machine learning models that inform product direction.
- Ability to work from the San Francisco office three days per week or be based elsewhere in the United States under case-by-case consideration.
Nice to have
- Cloud platform expertise with AWS, GCP, or Azure.
- Knowledge of dbt, Kubernetes, Terraform, CI/CD, and DevOps practices.
- Experience with knowledge worker productivity tools and D3.js.
Culture & Benefits
- Collaborative, inclusive, and fast-paced startup environment.
- Equity and comprehensive medical, dental, and vision coverage with covered premiums.
- 401(k), wellness, commuter, and FSA stipends.
- Bi-annual in-person company off-sites.
- Visa sponsorship and relocation assistance are not available.
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