Lead Data Engineer (Data Engineer)

Department

KELLEY SCHOOL OF BUSINESS (BL-BUS-IUBLA)

Department Information

The Kelley School of Business at Indiana University (IU) is a comprehensive provider of top-tier business education with a full portfolio of undergraduate, graduate, doctoral, and executive programs on campuses in Bloomington and Indianapolis, and online worldwide. The Kelley School has been creating career momentum for 100 years, going from 70 students in 1920 to an enrollment of more than 15,000 students today. Our innovative curriculum is built on personal development, teamwork, and experiential learning with an emphasis on global and social responsibility. Our success is reflected in our reputation among academic peers, our career placement statistics, and the accomplishments of more than 137,000 living alumni around the globe.

Job Summary

Performs advanced data management tasks, including complex data modeling, conversion, de-duplication, migration, and identification and repair of data quality issues.
Designs, develops, and implements complex custom data systems and advanced reconciliation tools, processes, rules, solutions etc. to validate data, match/merge, and upload batch lists.
Creates and tunes highly complex stored procedures and queries for advanced data management and extraction.
May contribute to committees and communities of practice to share and improve data engineering practices across the university; provides a high level of consultation and mentoring to other groups and staff on the use of data engineering tools and software.
Makes recommendations to improve, as well as implements, documentation and security protocols and procedures for data engineering projects and/or activities; fixes complex problems and resolves issues accordingly.
Provides advanced troubleshooting and problem analysis/resolution for data related issues; acts as a point of escalation for junior team members; performs advanced scripting and modifications of application and products for corrective action.
Performs advanced-level research and stays up-to-date with data engineering best practices and approaches; stays abreast of latest security threats and risks to proactively address potential exposures.
May serve as project lead; often provides guidance to junior peers.

Qualifications

Combinations of related education and experience may be considered. Education beyond the minimum required may be substituted for work experience. Work experience beyond the minimum required may be substituted for education.

EDUCATION

Required

Bachelor's degree (preferably in computer science, information science, or related field.)

WORK EXPERIENCE

Required

5 years data management, engineering, or related experience.

SKILLS

Required

Proficient communication skills.
Maintains a high degree of professionalism.
Demonstrates time management and priority setting skills.
Demonstrates a high commitment to quality.
Possesses flexibility to work in a fast paced, dynamic environment.
Seeks to acquire knowledge in area of specialty.
Highly thorough and dependable.
Demonstrates a high level of accuracy, even under pressure.
Possesses a high degree of initiative.
Ability to influence internal and/or external constituents.

Preferred

Familiarity with conventional machine learning models and processes, including classification, clustering, and retrieval.
Demonstrated experience integrating AI/LLM models (e.g., LLaMA via Ollama, OpenAI, Claude, or Amazon Bedrock) into enterprise platforms and services.
Ability to process data and engineer features for downstream model integrations.
Experience with prompt engineering, inference pipelines, and APIs for deploying AI agents at scale.
Experience with fine-tuning and/or supervising model adaptation to domain-specific data and use cases.
Experience with agentic frameworks such as Langgraph, Diffy, or related technologies.
Knowledge on tracking data and model lineages including the ability to ensure security and compliance of the deployed models to comply with appropriate governance, to manage role-based access to services, and to apply data masking where necessary.
Ensuring security and compliance of the deployed models to comply with appropriate governance, managing role-based access to services, and applying data masking where necessary.
Experience building or consuming data virtualization layers to abstract complex enterprise data systems for AI consumption.
Strong understanding of modern data architectures (data lakes, lakehouses, vector stores) and API-first approaches to data access and transformation.
Familiarity with vector databases (e.g., Marqo, Milvus, Pinecone) and embedding models for semantic search and retrieval-augmented generation (RAG).
Proficiency in building and maintaining RESTful APIs that interface with internal systems, enabling AI-enhanced features in existing applications.
Experience with cloud services (Azure, AWS), containerized deployments (e.g., Docker), and CI/CD workflows.
Comfort working with both in-house developed tools and open-source AI/ML infrastructure.
Openness to hybrid .NET and Python ecosystems to support the evolving needs of our AI integration efforts.
Passion for supporting digital transformation through AI education and enablement across diverse stakeholder groups, including non-technical faculty and staff.
Demonstrated ability to explain complex technical concepts to a wide range of audiences, and a commitment to building reusable resources, guides, and documentation.
Strong curiosity, experimental mindset, and a desire to shape the future of AI in higher education.
Interest in bridging research, teaching, and operations with intelligent systems and modern data practices.
Ability to work iteratively in a fast-moving environment where priorities evolve as innovation unfolds.

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