We are seeking an experienced Analytics Engineer III for a contract-to-hire opportunity with a large, well-established organization in the higher education space. This role will focus on building and maintaining analytics-ready data models, semantic layers, and data pipelines using SQL, Python, dbt, Dagster, and AWS. The position combines analytics engineering, data engineering, and AI to transform raw data into trusted, reusable data products.
This is an opportunity to work with modern data technologies and help shape how AI and analytics are used across a complex organization. You will collaborate with data engineers, AI engineers, analysts, and business stakeholders to build the foundation for reporting, machine learning, and AI-powered applications. The ideal candidate enjoys hands-on engineering, solving complex data challenges, and exploring emerging technologies like LLMs, RAG, embeddings, and AI agents. This hybrid opportunity also offers professional development and the potential for long-term growth.
Contract Duration: Contract-to-hire
Required Skills & Experience - 5+ years of experience in analytics engineering, data engineering, business intelligence development, or a related technical field.
- Advanced SQL and Python skills.
- Experience building data models and transformations using dbt or similar tools.
- Strong understanding of data modeling, data architecture, and analytics engineering principles.
- Experience with Git, code reviews, automated testing, and CI/CD.
- Experience building, maintaining, and troubleshooting data pipelines.
- Experience with cloud-based data platforms or lakehouse environments.
- Understanding of data quality, documentation, governance, privacy, and security best practices.
- Ability to independently deliver complex technical solutions.
- Strong communication and cross-functional collaboration skills.
Desired Skills & Experience - Experience with AWS, particularly S3.
- Familiarity with Apache Iceberg and Trino.
- Experience with Dagster or similar orchestration tools.
- Experience building semantic layers and standardized business metrics.
- Exposure to LLMs, RAG, embeddings, AI agents, or AI-driven analytics.
- Experience with metadata, data lineage, and context engineering for AI applications.
- Familiarity with machine learning pipelines and feature engineering.
- Experience with data contracts, observability, and automated data quality testing.
- Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field, or equivalent experience.
What You Will Be Doing Tech Breakdown
- SQL, Python, and dbt
- AWS, S3, Apache Iceberg, and Trino
- Dagster and data pipeline orchestration
- Semantic layers, LLMs, RAG, and AI-enabled analytics
Daily Responsibilities
- 60% Hands-On Engineering: Build, test, and optimize data models, SQL transformations, semantic layers, and data pipelines.
- 25% AI & Data Enablement: Develop curated datasets, metadata, and structured context to support AI agents, LLMs, RAG workflows, and machine learning applications.
- 15% Collaboration & Engineering Standards: Work with technical and business teams to define requirements, improve data quality, maintain documentation, and follow software engineering best practices.
