Business Intelligence Engineer

Zymo Research Corporation•Tustin, CA

1d•$75,000 - $115,000•Onsite

About The Position

Zymo Research is seeking a Business Intelligence Engineer to contribute to data pipelines and frameworks that support Commercial Operations with rigorous, decision-oriented analytics. This role is accountable for converting complex commercial, marketing, and external data into structured insights that inform leadership decisions, prioritize initiatives, and improve commercial execution. This is a hands-on, code-first role, not just a dashboarding or reporting role. The ideal candidate is someone who enjoys writing code and building analytical solutions, who is comfortable with ambiguous business intelligence data, and who can translate data into clear recommendations for senior stakeholders. This role operates at the intersection of analytics, engineering and commercial strategy, with direct exposure to executive leadership.

Requirements

BS or MS in Data Science, Computer Science, Bioinformatics, Engineering, or a related quantitative field.
Demonstrated experience building scripts, pipelines, or small applications that pull together data from multiple sources.
Strong proficiency in Python, and its data science libraries including pandas and NumPy.
Experience extracting and integrating data via APIs and web-based tools such as Beautiful Soup or Selenium.
Working knowledge of SQL (Structured Query Language) or structured data querying.
A self-directed builder who enjoys turning an open-ended business questions into working code.
Proven experience presenting analyses and recommendations to senior or executive stakeholders.
Strong business judgment and ability to connect data to commercial outcomes.
High attention to detail, intellectual rigor, and comfort operating with imperfect information.
Interest in commercial strategy, revenue operations, and business intelligence.

Nice To Haves

Experience in management consulting, internal strategy, or analytics-heavy advisory roles is strongly preferred.
Experience in commercial analytics, life sciences, or scientific data environments is a plus.
Experience using AI tools and workflows in commercial data analytics is strongly preferred.
Familiarity with data warehouse software (Snowflake or ClickHouse), web frameworks (Flask, React) and containerization tools (Docker) is a plus.
Exposure to graph-based data models or network-style analysis is beneficial but not required.

Responsibilities

Extract, integrate, and transform data from internal systems and external sources using APIs, automated scripts, and web-based tools.
Build scalable ETL workflows that combine commercial, scientific, and market data into reliable analytical datasets.
Automate recurring analyses and reporting to improve efficiency, consistency, and scalability.
Apply disciplined data governance practices to ensure accuracy, transparency, and reproducibility.
Contribute to the design and development of a business intelligence data warehouse.
Structure ambiguous business questions into clear analytical problem statements, hypotheses, and workplans.
Design and deliver dashboards, scorecards, and analyses that track commercial performance and surface actionable insights.
Analyze trends, drivers, and variances, with a focus on explaining implications, not just reporting results.
Conduct ad hoc analyses and deep dives to support strategic decisions, performance reviews, and opportunity prioritization.
Apply statistical and analytical techniques to support forecasting, segmentation, and performance diagnostics.
Build lightweight tools or applications to support internal analytics use cases where appropriate.
Explore the use of AI tools and agents to accelerate analysis and insight generation. Build successful cases into scalable AI workflows.
Partner closely with Commercial Operations, Marketing, Sales, and leadership to clarify objectives, pressure-test assumptions, and refine analytical approaches.
Prepare and deliver executive-ready presentations that synthesize insights, trade-offs, and recommendations.
Communicate complex analyses clearly to senior leaders, focusing on business impact and decision relevance.
Act as a thought partner to stakeholders, not just a reporting function.