Data Engineer II

Deloitte’s HC Forward team is an innovation partner focused on modernizing Human Capital through AI, data, and engineering. In this Data Engineer II role, you will help design and operationalize modern data foundations that support LLM-powered applications and analytics products used by clients across industries.

This onsite position is based in Indianapolis, IN and includes an annual wage range of USD 91,100 to 179,500 (based on a wide range of factors used in compensation decisions). You will partner with Human Capital product managers, clients, and cross-functional teams to deliver end-to-end solutions, from data modeling to executive-ready dashboards and stakeholder communications.

What you’ll do

• Lead the design, development, and delivery of analytics solutions for complex workforce and Human Capital challenges across client engagements.
• Build and maintain scalable data pipelines, dashboards, and reporting frameworks using SQL and Tableau (or equivalent visualization tools).
• Turn ambiguous business problems into structured analytical approaches, then communicate findings and recommendations to technical and non-technical stakeholders.
• Collaborate across service lines to embed AI-enabled capabilities and emerging data methodologies into client solutions.
• Design and deliver intuitive, executive-ready reports and dashboards that make complex workforce data accessible and actionable.
• Support business development activities, including proposals, client presentations, and thought leadership content.
• Enable AI, ML, and GenAI solutions through data architecture and engineering delivery, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption.
• Operationalize modern data and retrieval foundations for LLM-powered applications, including RAG, embeddings, vector search, and governed access to structured and unstructured data.
• Own end-to-end delivery with Human Capital product managers and clients, from data modeling through stakeholder presentations.
• Partner with data science, ML engineering, and product stakeholders to productionize use cases using pipelines and platforms with strong governance, lineage, quality controls, and monitoring.

What you’ll bring

• 2+ years of experience in data science, data engineering, or analytics.
• 2+ years of modern data and AI engineering, including data modeling, batch or streaming pipelines, structured and unstructured processing, and feature engineering or serving fundamentals.
• 1+ year of experience with machine learning concepts, large language model applications, or AI-augmented analytics workflows.
• 2+ years using Python or R for statistical modeling and data wrangling, with proficiency in Databricks or Snowflake.
• Ability to work independently and collaborate as part of a team.
• Effective written and verbal communication skills, including the ability to provide clear guidance to others.
• Meticulous attention to detail and commitment to quality.
• Ability to lead projects or workstreams, manage and prioritize multiple tasks in a fast-paced environment, meet deadlines, and maintain professional relationships.
• Ability to travel 0–25% on average, based on work and client needs.
• Legal authorization to work in the United States without employer sponsorship, now or in the future.
• A bachelor’s degree in a STEM field (for example, Computer Science, Engineering, Statistics, Data Science, or related).

Technologies you may work with

• SQL, Tableau, Python, R
• Databricks, Snowflake
• Claude, GPT/Codex, Gemini
• RAG, embeddings, vector search

Preferred qualifications

• Advanced degree (MS or PhD) and/or relevant certifications.
• Client-facing or consulting experience, particularly with Human Capital platforms and integrations (for example, Workday, SAP SuccessFactors, Oracle HCM, Salesforce) or HR data domains.
• Experience with LLMOps and MLOps capabilities, including evaluation, monitoring, governance workflows, and model or prompt or version management.
• Cloud experience with AWS, Azure, or GCP, including managed data platforms and scalable compute patterns.
• Experience explaining complex data and technology concepts, articulating risks and trade-offs, documenting decisions, and collaborating across teams (product, data science/ML, data engineering, platform, security).
• Experience building repeatable analytics products (tools, models, or platforms) and familiarity with ethics and privacy practices, including consent, PII, and bias boundaries.