Business Intelligence
Plaid
Operations, Data Science
San Francisco, CA, USA
Posted on Jul 4, 2025
We are the Business Intelligence team, a full-stack analytics engineering team that owns the data pipeline from foundational data to trusted dashboards across Product, Marketing, and Go-To-Market (GTM) at Plaid. Our aim is to create the infrastructure, data models, and self-serve systems that enable stakeholders to confidently make decisions based on reliable, accessible data.
As a member of Business Intelligence working directly with our Network Enablement product team, you will be at the forefront of building scalable and reliable analytics foundations that power decision-making across Product, Engineering, and Data Science. This senior individual contributor role is focused on developing foundational product-data infrastructure, defining and maintaining trusted metrics, and enabling self-serve analytics and AI tooling.
Responsibilities
- Build and maintain foundational dbt models and curated, source-of-truth datasets that support analytics, experimentation, and decision-making across Product, Engineering, and Data Science.
- Collaborate with Product Managers, Engineers, and Data Scientists to translate instrumentation and analytical requirements into well-structured data models.
- Develop and enforce standards for metrics, modeling practices, and documentation to ensure consistency and reusability.
- Own the full analytics engineering lifecycle—from raw ingestion and transformation to surfacing insights in BI tools like Tableau or Mode.
- Partner with Data Science teams to enable experimentation, forecasting, and AI tooling with reliable, structured data.
- Partner with Data Engineering to ensure data quality and observability, including testing, alerting, and documentation.
- Enable self-serve analytics by building semantic layers and reusable data products that empower teams to independently access trusted insights.
- Act as a technical thought partner within the product data domain, contributing to roadmap planning and data architecture decisions.
- Unlock insights by ensuring Data Science has clean, structured, and trustworthy data.
- Reduce engineering and analytics rework by creating centralized and well-documented metrics.
- Accelerate product iteration cycles by enabling Product teams to self-serve key usage and performance metrics.
- Improve confidence and consistency in decision-making through robust, well-governed data systems.
Impact:
Qualifications
- 10+ years of experience in analytics engineering, data engineering, or a related technical data role.
- Deep expertise with SQL and dbt, including modular modeling, documentation, and testing.
- Proven track record of building source-of-truth data models that support product use cases.
- Experience with cloud data warehouses (e.g., Databricks, Snowflake, Redshift, or BigQuery), semantic layers (dbt), version control (Git), and visualization tools (Tableau, Looker).
- Familiarity with instrumentation design and working with product logs or event data.
- Experience partnering with Product, Engineering, and Data Science stakeholders to translate business logic into technical data solutions.
- Strong focus on data quality, governance, and scalability in analytics workflows.
- Clear, concise communication skills and a collaborative, systems-oriented mindset.