Armada - AI Intern, Data Engineering & Agent Workflows (Unpaid)
Requirements
• Pursuing or recently completed a degree in Computer Science, Data Science, Data Engineering, Information Systems, Analytics, or a related field. • Strong Python skills, including experience with libraries like Pandas, requests, and FastAPI or Flask. • Solid SQL skills — able to write and debug analytical queries (joins, window functions, CTEs) against real-world, messy data. • Familiarity with the modern data stack (dbt, a columnar warehouse such as StarRocks/Snowflake/BigQuery/Databricks, and a BI tool such as Omni, Tableau, Looker, or Power BI). • Hands-on experience building something with an LLM API (Claude, OpenAI, etc.) — even a side project counts. Familiarity with prompt design, tool/function calling, or RAG is a strong plus. • Exposure to workflow automation tools (Power Automate, Zapier, n8n, Airflow, or similar). • Comfort with Git and basic software engineering practices (branches, PRs, code reviews). • Strong problem-solving and analytical skills; able to work independently and in a team-oriented environment. • What You’ll Gain: • What You’ll Gain: • Hands-on experience working with modern AI, automation, and data engineering tools in a real-world business environment • Exposure to cutting-edge technologies including LLMs, workflow automation, and cloud data platforms • The opportunity to build impactful projects used by GTM, RevOps, Finance, and leadership teams • Mentorship and collaboration with experienced engineers and business leaders • Practical experience with Python, SQL, dbt, APIs, dashboards, and AI-powered workflows • A stronger technical portfolio and resume through meaningful, production-focused projects • Insight into how AI and data systems drive decision-making and operational efficiency in a fast-growing technology company • Professional development in problem-solving, communication, collaboration, and cross-functional teamwork • You're a Great Fit if You're • A go-getter with a growth mindset. You're intellectually curious, have strong business acumen, and actively seek opportunities to build relevant skills and knowledge • A detail-oriented problem-solver. You can independently gather information, solve problems efficiently, and deliver results with a "get-it-done" attitude • Thrive in a fast-paced environment. You're energized by an entrepreneurial spirit, capable of working quickly, and excited to contribute to a growing company • A collaborative team player. You focus on business success and are motivated by team accomplishment vs personal agenda • Highly organized and results-driven. Strong prioritization skills and a dedicated work ethic are essential for you • Equal Opportunity Statement
Responsibilities
• Build and maintain data pipelines across Salesforce, SharePoint, Excel, and operational systems into our StarRocks data warehouse using dbt and Python. • Develop and ship AI agents and copilots using the Claude API (and other LLM tooling) for use cases like lead scoring, account research, call analysis, and internal Q&A over company data. • Design and automate cross-system workflows using Power Automate, Teams Adaptive Cards, and lightweight Python/FastAPI services to replace manual, spreadsheet-driven processes. • Model business data in dbt and Omni — building reusable semantic models, metrics, and dashboards that GTM, RevOps, and leadership rely on. • Enrich and clean prospect and account data using sources like ZoomInfo, LinkedIn Sales Navigator, and public government datasets (e.g., USASpending, SAM.gov, FCC, EIA, FERC, SEC EDGAR). • Prototype and ship internal tools end-to-end — backend (Python/FastAPI), frontend (Vite/React), and storage (PostgreSQL, StarRocks) — for AI-prioritized daily workflows. • Write clear documentation for data models, pipelines, agents, and workflows so the team can extend and maintain them. • Collaborate with GTM, RevOps, Finance, and product teams to translate business questions into data and automation solutions. • Stay current on advances in agentic AI, retrieval, evals, and the modern data stack — and bring useful ideas back to the team.
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