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 lifecyclefrom 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.
Impact:
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.
Qualificaitons
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.
$159600 - $260400 a year
The target base salary for this position ranges from $159600/year to $260400/year in Zone 1 in Zone 4 or encompassing all Zones. The target base salary will vary based on the jobs location.
Our geographic zones are as follows:
Zone 1 - New York City and San Francisco Bay Area
Zone 2 - Los Angeles Seattle Washington D.C.
Zone 3 - Austin Boston Denver Houston Portland Sacramento San Diego
Zone 4 - Raleigh-Durham and all other US cities
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan including medical dental vision and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position candidates work experience and skillset and location. Pay and benefits are subject to change at any time consistent with the terms of any applicable compensation or benefit plans.
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