Business Intelligence Analyst / Analytics Engineer
About The Position
This role is a hybrid of a Business Intelligence Analyst and an Analytics Engineer. It involves designing and maintaining clean, scalable data models, transforming raw data into well-structured datasets for analytics and reporting, and partnering with engineering to define data pipelines and improve data architecture. The position is responsible for ensuring data quality, testing, and documentation across datasets, and building and maintaining a single source of truth for key business metrics. Additionally, the role focuses on building dashboards and reporting for various teams and leadership, analyzing complex datasets to identify trends and opportunities, translating ambiguous business problems into structured analysis, defining and standardizing core metrics, and supporting experimentation and product decision-making with data. A significant aspect of the role is cross-functional collaboration to improve data accessibility, enabling teams to self-serve data through well-designed models and dashboards, and acting as a bridge between technical data systems and business needs. Unlike a standard BI Analyst who primarily builds dashboards and answers questions, this role defines the data models behind those dashboards, owns data quality and metric definitions, and builds infrastructure that scales with the company.
Requirements
- 3+ years of experience in BI, data analytics, or analytics engineering
- Strong SQL skills (advanced querying, joins, performance optimization)
- Experience building data models and working with transformation tools (e.g., dbt preferred)
- Experience with BI tools (Looker, Tableau, Power BI, etc.)
- Experience working in a startup or high-growth environment
- Strong understanding of data warehousing concepts
- Ability to own problems end-to-end (data model insight impact)
- Strong communication skills across technical and non-technical audiences
Nice To Haves
- Experience with modern data stack (dbt, Snowflake, BigQuery, Redshift)
- Familiarity with Python for data workflows
- Experience working with product or operational analytics
- Exposure to event tracking or analytics instrumentation
- Background in SaaS, fintech, or data-heavy industries
Responsibilities
- Design and maintain clean, scalable data models (e.g., using dbt or similar tools)
- Transform raw data into well-structured datasets for analytics and reporting
- Partner with engineering to define data pipelines and improve data architecture
- Ensure data quality, testing, and documentation across datasets
- Build and maintain a single source of truth for key business metrics
- Build dashboards and reporting for product, operations, and leadership
- Analyze complex datasets to identify trends and opportunities
- Translate ambiguous business problems into structured analysis
- Define and standardize core metrics (e.g., turnaround time, throughput, client performance)
- Support experimentation and product decision-making with data
- Collaborate with engineering and stakeholders to improve data accessibility
- Enable teams to self-serve data through well-designed models and dashboards
- Act as a bridge between technical data systems and business needs
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
