Job Description: Senior Analytics Engineer, Network Enablement

Employment Type: Full-time

Position Overview

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.

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.

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.

Compensation & Benefits

Salary Range: $159,600 - $260,400 per year

• The target base salary for this position ranges from $159,600/year to $260,400/year.
• The target base salary will vary based on the job's location and geographic zone.
  • 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 of equity and/or commission may be offered depending on the position.
• Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k).
• Pay is based on factors such as (but not limited to) the candidate's qualifications, experience, and location.

Location Type

• Not specified in the provided text.