Senior Analytics Engineer

We are seeking a Senior Analytics Engineer for a contract-to-hire opportunity with a well-established organization in the higher education space. This role will focus on leading the design of analytics data architecture, semantic models, and data pipelines using SQL, Python, dbt, Dagster, and AWS. The position combines analytics engineering, data engineering, and AI to build scalable, trusted data products that support reporting, machine learning, and AI-powered applications.

This is an opportunity for a senior engineer to take technical ownership of complex data initiatives and help shape how an organization uses data and AI. You will define engineering standards, architect semantic layers, and develop the context that allows LLMs and AI agents to accurately interpret and query data. Working closely with data engineers, AI engineers, and business stakeholders, you will also mentor other engineers and influence technical strategy. The role offers hands-on work with modern data technologies, exposure to emerging AI applications, and the opportunity to help build the foundation for future analytics and AI solutions in a collaborative, hybrid environment.

Contract Duration: Contract-to-hire

Required Skills & Experience

• 8+ years of experience in analytics engineering, data engineering, or a related technical field.

• Proven experience leading complex technical projects and making data architecture decisions.

• Expert-level SQL and Python skills.

• Extensive experience building data models and transformations using dbt or similar tools.

• Deep understanding of data architecture, dimensional modeling, lakehouse architecture, and semantic layer design.

• Advanced software engineering practices, including Git, code reviews, automated testing, CI/CD, and pipeline orchestration.

• Experience designing, building, and optimizing scalable data pipelines.

• Strong understanding of data quality, governance, privacy, security, and compliance.

• Experience establishing engineering standards and conducting technical design and code reviews.

• Strong communication skills and experience collaborating with technical and non-technical stakeholders.

• Experience mentoring engineers and providing technical guidance.

Desired Skills & Experience

• Experience with AWS, particularly S3.

• Expertise with Apache Iceberg, Trino, and cloud lakehouse environments.

• Experience with Dagster or similar orchestration platforms.

• Experience designing enterprise semantic layers, metrics frameworks, and metadata management practices.

• Experience enabling AI and ML use cases, including LLM integration, RAG, embeddings, and ML pipelines.

• Familiarity with context engineering, knowledge graphs, MCP, and text-to-SQL applications.

• Experience developing evaluation frameworks to measure the accuracy and reliability of AI-driven analytics.

• Experience implementing data contracts, lineage, observability, and enterprise data quality frameworks.

• Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field; advanced degree preferred, or equivalent experience.

What You Will Be Doing

Tech Breakdown

• SQL, Python, and dbt for analytics data architecture and modeling

• AWS, S3, Apache Iceberg, and Trino for cloud lakehouse solutions

• Dagster and AWS for production-grade data pipelines

• Semantic layers, metadata, MCP, text-to-SQL, LLMs, RAG, and AI-enabled analytics

Daily Responsibilities

• 30% Data Architecture & Modeling: Architect scalable data models and lakehouse layers, establish modeling standards, and resolve complex data design and performance challenges.

• 25% Semantic Modeling & AI Context: Define enterprise metrics, business logic, semantic layers, metadata, and AI-ready interfaces that enable reliable analytics and AI applications.

• 20% Data & ML Pipeline Engineering: Design and optimize production pipelines supporting analytics, feature engineering, model training, scoring, and embedding generation.

• 10% Engineering Standards & Governance: Establish best practices for testing, CI/CD, code reviews, data quality, observability, and data governance.

• 15% Technical Leadership & Collaboration: Lead complex technical initiatives, mentor engineers, collaborate across data and AI teams, and contribute to the organization's analytics and AI strategy.