Associate Data & AI Engineer

Overview We are seeking an Associate Data & AI Engineer to help build and scale the data and AI infrastructure that powers our analytics, automation, and decision-making. This role sits at the intersection of data engineering and applied AI, with a specific focus on deploying LLM-powered agents that automate high-value workflows across the organization. This role is ideal for an early-career engineer who wants hands‑on experience across data engineering, AI agent development, and marketing analytics. You will work closely with cross‑functional teams to design data pipelines, build and deploy LLM-based automation agents, and deliver high-quality datasets and reporting that drive business impact. Real‑world use cases include automating paid media reporting, daily KPI distribution, and performance dashboards. What You’ll Do Data Engineering & Infrastructure Design, build, and maintain scalable data pipelines (batch and real‑time) Develop ETL/ELT workflows to ingest and transform data from multiple sources Support the development and optimization of data warehouses and data models Ensure data quality, reliability, and performance across systems AI & Data Applications Design and deploy LLM-powered agents to automate repeatable reporting workflows, including paid media performance summaries and daily KPI digests Collaborate on building AI-driven tools and business dashboards Product & Business Impact Partner with greater data and analytics team to deliver clean, usable datasets Translate business needs into scalable data solutions Enable analytics, reporting, and AI tools Engineering & Collaboration Contribute to testing, and debugging Participate in code reviews and maintain high engineering standards Document workflows, pipelines, and project progress Support monitoring, observability, and continuous improvement of data systems What We’re Looking For Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field 0–2 years of experience (internships, projects, or coursework included) in data engineering, software development, or data science Proficiency in Python and SQL Familiarity with data pipelines, data modeling, and ETL processes Basic understanding of cloud platforms (AWS, GCP, or Azure) Exposure to data warehouses (e.g., Snowflake, BigQuery, Redshift) Understanding of machine learning concepts (e.g., NLP, neural networks, or data science fundamentals) Familiarity with LLM APIs and prompt engineering (e.g., OpenAI, Anthropic) with an understanding of how to build reliable AI-driven workflows Strong problem‑solving skills and ability to work both independently and collaboratively Nice to Have Experience with orchestration tools (Airflow, Dagster) Experience with Digital Analytics (Adobe), Social Analytics (Sprout) and Business Intelligence (DOMO, PowerBI) Exposure to real‑time or event-driven systems Experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, or CrewAI) Familiarity with workflow automation tools (n8n, Make, or Zapier) Portfolio or projects demonstrating data or AI work Comp: TBD (will update shortly) #J-18808-Ljbffr Career Techniques