The Role
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.
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The Company
What We Do
Plaid is used by thousands of digital financial apps and services like Betterment, Expensify, Microsoft and Venmo, and by many of the largest banks to make it easy for consumers to connect their financial accounts with the apps and services they want to use. Plaid connects with over 11,000 financial institutions across the U.S, Canada and Europe.
At Plaid, we have diverse backgrounds and skills, but we're all passionate about building a more efficient and inclusive financial infrastructure—together.
