Business Intelligence Engineer
| Company | DoorDash |
|---|---|
| Location | Seattle, WA, USA, San Francisco, CA, USA, Sunnyvale, CA, USA |
| Salary | $117500 – $256500 |
| Type | Full-Time |
| Degrees | |
| Experience Level | Mid Level |
Requirements
- 3+ years experience working in business intelligence, data analytics, Data engineering or a similar role
- Strong SQL skills and experience with data modeling techniques (e.g., dimensional modeling, 3 Nf, data vault)
- Proficiency in a programming language such as Python or Scala
- Experience building reporting and dashboarding solutions using data lake/Snowflake or similar ecosystem
- Expert in Database fundamentals, SQL and performance tuning
- Experience in maintaining data integrity and reliability by leveraging data quality frameworks and orchestration tools for seamless pipeline execution
- Excellent communication skills and experience working with technical and non-technical teams
- Comfortable working in fast paced environment, self starter and self organizing
- Ability to think strategically, analyze and interpret market and consumer information
Responsibilities
- Design, develop, and maintain robust data models to support analytical and product data needs across the organization
- Collaborate with data engineers, data scientists, and business stakeholders to understand data requirements and translate them into scalable data solutions
- Implement and optimize ETL/ELT processes to ensure data quality, reliability, and performance
- Own and define business KPIs, their measurement plans, data requirements and reporting
- Build processes to ensure correct, timely and reliable reporting
- Address ad-hoc reporting requirements and find pathways for automation
- Build and enforce common design patterns to increase report reusability, readability and standardization
- Build visually appealing, high-performing, and impactful reporting/dashboard products using tools like Tableau/Sigma across large data sets
Preferred Qualifications
- Experience with real-time data processing and streaming technologies
- Experience with modern data warehousing platforms (e.g., Snowflake, DataBricks, Redshift) and knowledge of data visualization tools (e.g., Looker, Tableau)
- Familiarity with machine learning concepts and their data requirements