Data Engineering Analyst

Tiverton Advisors•Raleigh, NC

2h•Onsite

About The Position

Tiverton is seeking a Data Engineering Analyst (Entry Level / Intern) to support our investment process and portfolio operations through data engineering, analytics, and AI-powered automation. This is an entry-level position ideal for recent graduates or current students seeking internship-to-hire opportunities. The role combines data infrastructure development with investment analytics, working across deal sourcing, due diligence, portfolio monitoring, and LP reporting. The ideal candidate is curious, eager to learn, and excited to build solutions across the full data stack - from pipeline engineering to business intelligence - while applying AI/ML tools to solve real-world problems in agricultural private equity. This role offers broad exposure to both the investment side (deal flow, due diligence and fund analytics) and operations side (portfolio company data, reporting automation, and other analytics. This role is onsite in our Raleigh, NC office. The successful candidate will be self-motivated and energized by working with a group of thoughtful, smart, and skilled colleagues. He or she will enjoy being a part of a young, hungry and collaborative organization focused on becoming the pre-eminent investment firm in US agriculture.

Requirements

Proficiency in Python and SQL through coursework or projects; familiarity with pandas, APIs, or automation a plus
Exposure to data pipelines, ETL concepts, or data engineering workflows through coursework or projects
Familiarity with cloud platforms or data warehouses (Snowflake, BigQuery, AWS) – exposure through coursework or certifications counts
Interest in data visualization; experience with any BI tool (Power BI, Tableau, Looker) or willingness to learn
Solid Excel skills including formulas, pivot tables, and basic data analysis
Exposure to APIs, web scraping, or data collection methods (REST APIs, Beautiful Soup, or similar)
Interest in AI/ML tools and LLMs; experience with ChatGPT, Claude, or similar for productivity is a plus
Git version control and collaborative development workflows
Ability to translate business problems into technical solutions
Strong problem-solving skills - can debug data issues independently
Understanding of financial concepts and private equity metrics helpful but not required
Strong communication skills - can explain technical concepts to non-technical stakeholders
Self-directed with ability to prioritize and manage multiple projects
Detail-oriented with focus on data quality and reliability
Current senior or recent graduate (within 1 year) pursuing or holding a degree in a relevant field
Pursuing or recently completed Bachelor’s degree in Computer Science, Data Science, Engineering, Finance, Economics, or related quantitative field
Demonstrated interest through coursework, personal projects, hackathons, or prior internships involving data pipelines, analytics, or automation

Nice To Haves

Experience building LLM-powered applications or automation tools
Familiarity with CRM systems (Affinity, Salesforce) or investment workflow tools
Experience with document processing and unstructured data extraction
Knowledge of ML libraries (scikit-learn, numpy) and model deployment
Exposure to private equity, venture capital, or investment banking
Understanding of DevOps practices - testing, monitoring, CI/CD
Knowledge of agricultural markets, farm credit systems, or commodity data

Responsibilities

Build and maintain ETL pipelines pulling data from internal and external sources into our Snowflake data warehouse
Develop Python and SQL automation scripts for recurring data processes
Manage Snowflake data warehouse - schema design, query optimization, and data modeling
Build API integrations for third-party data sources (pricing data, B2B data providers, market intelligence)
Implement data quality checks, validation rules, and monitoring to ensure pipeline reliability
Create web scraping solutions for data collection from public sources
Maintain code repositories with proper version control and documentation
Support deal pipeline analytics and sourcing workflows in our CRM
Build models and analytics for sector trends (crop prices, land values, farm credit metrics)
Extract and analyze data from appraisal documents, financial statements, and industry reports
Develop due diligence analytical frameworks and data rooms for new investments
Create LP reporting dashboards and automated quarterly reporting processes
Support investment team with ad-hoc analytical requests and data visualization
Leverage LLMs (OpenAI, Claude) to accelerate document analysis, data extraction, and research workflows
Build AI-powered automation for deal screening, document processing, and data enrichment
Implement intelligent solutions for pattern recognition, anomaly detection, and data quality
Use prompt engineering and AI coding assistants to rapidly prototype analytical tools
Develop RAG (Retrieval-Augmented Generation) systems for knowledge management
Support portfolio company reporting requirements and data requests
Build dashboards and reporting tools for portfolio operations teams
Troubleshoot data issues and provide technical support to portfolio companies
Partner with investment team to ensure clean, reliable data for portfolio monitoring