What Will You Do in This Role?The Business Intelligence Engineer plays a vital role in driving our data and analytics infrastructure forward. You will partner closely with data engineers, analysts, product managers, and business stakeholders to architect robust data models, streamline transformation layers, and deliver high-impact insights. This role is ideal for a builder who is fluent in both data architecture and analytics, and who thrives in a fast-paced environment where they can guide data strategy.
Okay, but what will you actually do in this role?- Serve as a senior BI partner for the Product team, owning data architecture, guiding data strategy, pipeline reliability, and the analytics engineering roadmap in support of business unit goals.
- Collaborate and consult directly with business teams to understand their strategy, economics, and goals, translating business questions into analytical frameworks.
- Design, build, and maintain scalable data pipelines and transformation layers (such as dbt models and ELT workflows) that power dashboards, reports, and ML features.
- Develop and maintain data marts, semantic layers, and self-serve tooling that empowers internal stakeholders to make smarter, faster decisions.
- Partner with analysts and product managers to instrument, design, and support A/B testing frameworks and experimentation infrastructure.
- Monitor data pipeline health by proactively identifying data quality issues and implementing robust observability and alerting frameworks.
- Work closely with data governance and data engineering to ensure data quality, lineage, and strict compliance with organizational standards.
- Apply ML engineering practices to productionize predictive models, support feature engineering pipelines, and facilitate audience segmentation and targeting workflows.
- Champion engineering best practices including peer code reviews, CI/CD for data pipelines, version control, and documentation standards.
- Stay informed about emerging trends in data science, analytics engineering, and the modern data stack.
You Might Be a Good Fit If You:- Are deeply curious and love to learn. You enjoy digging into systems to understand how they work and thrive when solving a hard infrastructure or data modeling problem.
- Value high-performance, cross-functional collaboration and approach stakeholders with a consultative mindset to communicate timelines, trade-offs, and technical constraints clearly.
- Consider yourself both a builder and a scientist, capable of designing systems that are both technically rigorous and business-oriented, with the ability to tell powerful stories through data.
- Take proactive ownership of data platform reliability, ensuring that pipelines and data models remain accurate, highly performant, and durable.
- Thrive on asking "why" and are constantly looking for ways to make data platform architectures more reliable and impactful.
Required Experience and Skills:- 7+ years of experience in data science, analytics engineering, or business intelligence engineering, with a proven track record of building scalable data infrastructure that drives business impact.
- Advanced proficiency in SQL for complex querying, data modeling, and robust pipeline development.
- Deep expertise in data transformation frameworks such as dbt (or equivalent).
- Strong experience with cloud data warehouses (such as Snowflake, BigQuery, Redshift, or Databricks), including performance tuning and cost optimization.
- Experience building and maintaining ELT/ETL pipelines using tools like Airflow, Prefect, dbt, or similar orchestration frameworks.
- Proficiency in Python for data pipeline development, automation, and ML feature engineering.
- Experience with BI and visualization tooling such as ThoughtSpot, Tableau, Looker, or Power BI.
- Experience with Git-based workflows, CI/CD for data pipelines, and Jira (or equivalent project management tools).
- Excellent communication and translation skills-the ability to articulate technical design decisions, trade-offs, and data quality issues clearly to both technical and non-technical audiences.
- Education: Bachelor's degree, preferably in computer science, data science, engineering, statistics, or a related field.
Nice-to-Have/Pluses:- Experience or familiarity with financial services/investing, digital publishing, direct response marketing, or subscription product environments.
- Familiarity with statistical testing, experiment design, A/B testing infrastructure, or ML/AI engineering practices (including model productionization, feature stores, and LLM-based tooling).
Compensation: Below is our target compensation range. While we are budget conscious, we're also eager to find the right person for this role, so if your target is outside of this range, please don't hesitate to apply and we'd be happy to have a conversation.
Hourly Pay Range
$80-$95 USD
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