Peter Causey

About Peter Causey

Analytics Engineer — dbt, SQL, Redshift, Python, Looker. Focused on dimensional modeling and reliable, trusted metrics.I build and maintain analytics data platforms that organizations rely on to make decisions.My work sits between data engineering and analytics: I model data in the warehouse, define business metrics, and maintain the semantic layer so teams can self-serve trustworthy reporting without ad-hoc analysis.I’ve owned the full analytics lifecycle:• ingesting data from operational systems• transforming and modeling data in dbt• defining company-wide KPI and metric definitions• building the Looker semantic layer• enabling self-service reporting across leadership, product, and operationsI focus on metric reliability and data trust. My goal is not producing dashboards — it is ensuring the business can operate without debating whose numbers are correct.I’m interested in roles centered on data modeling, metric definition, and enabling organizations to run on consistent, trusted data.Technical Stack• Data Warehousing: Amazon Redshift, PostgreSQL, MySQL, MariaDB• Transformation: dbt (modeling, testing, documentation)• BI & Semantic Layer: Looker, Tableau• Pipelines & Ingestion: Python data pipelines, Stitch ELT• Languages: SQL, Python• Development: Git, version-controlled analytics workflows• Data Modeling: Dimensional modeling (Kimball), fact & dimension design, metric definition• Analytics Modeling: Layered dbt modeling (staging → intermediate → marts → reporting)• Data Quality: dbt tests (uniqueness, relationships, null checks), upstream data validation

Experience

1. Lead Analytics Engineer

   Capitalize

   Dec 2022 — Present · New York, NY, US

   Analytics engineering responsibilities:* Analytics warehouse modeling (Amazon Redshift, SQL)* dbt transformations and warehouse data testing* Looker semantic layer (LookML) and metric definitions* Data pipelines and ELT ingestionInternal company title: Lead Analyst* Owned the Amazon Redshift analytics warehouse fed by replicated application data (PostgreSQL), product data (MongoDB), and external platforms (Stripe, Five9, ad/referral, Google Sheets/invoicing)* Developed Python ingestion pipelines and managed ELT workflows (Stitch), monitoring upstream schema changes and preventing reporting breakage* Leading migration to dbt-based transformations and warehouse data tests, moving validation from the BI layer to the warehouse* Built dimensional models and business marts supporting product funnel, lifecycle, and marketing analytics* Designed and maintained the Looker semantic layer (LookML explores and governed metrics) enabling self-service analytics across product, marketing, and operations* Partnered with product, operations, and leadership stakeholders to define and maintain trusted business metrics* Built operational staffing datasets aligning appointment demand forecasts with a 60-agent scheduling operation* Documented warehouse models and metrics for business users

Education

• UVA School of Data Science

  Masters, Data Science

• University of Maryland Baltimore County

  Bachelor of Science (BS), Psychology

  2008 — 2012

• IESE Business School

  Master of Business Administration (MBA)

  2016 — 2018

Skills

• Customer Service
• Management
• Mortgage Banking
• Loans
• Financial Analysis
• Analytical Skills
• Analysis
• Mortgage Lending
• Loan Origination
• Finance
• Banking
• Microsoft Office
• Sales
• Credit
• Financial Modeling