Data Engineer II

Join Deloitte to design and operationalize data foundations that enable AI, machine learning, and generative AI solutions across Human Capital.

Responsibilities

• Lead the design, development, and delivery of analytics solutions for workforce and human capital challenges across client industries.
• Build and maintain scalable data pipelines, dashboards, and reporting frameworks using SQL and Tableau (or equivalent visualization platforms).
• Convert ambiguous business needs into structured analytical approaches; communicate findings and recommendations to both technical and non-technical stakeholders.
• Collaborate across service lines to embed AI-enabled capabilities and emerging data methodologies into client solutions.
• Support business development with proposals, client presentations, and thought leadership content.
• Design and deliver intuitive, executive-ready reports and dashboards that make complex workforce data actionable.
• Support data architecture and engineering delivery to enable AI, ML, and GenAI solutions with trusted, secure, observable, and scalable data from ingestion to consumption.
• Design and operationalize modern data and retrieval foundations for LLM-powered applications, including RAG, embeddings, vector search, and governed access to structured and unstructured data.
• Work with Human Capital product managers and clients to deliver data-driven workforce analytics, owning solutions end-to-end from data modeling to stakeholder presentations.
• Build reusable analytics products (dashboards, reports, and components) to translate human capital data into clear insights.
• Partner with data science, ML engineering, and product stakeholders to implement production-ready pipelines and platforms with data governance, lineage, quality controls, and monitoring.

Requirements

• Independence and teamwork: ability to work independently and collaborate as part of a team.
• Communication: effective written and verbal communication skills.
• Quality: meticulous attention to detail and quality of work product.
• Stakeholder skills: ability to build and sustain professional relationships.
• Leadership: ability to lead projects or workstreams and provide clear guidance to others.
• Execution: ability to manage and prioritize multiple tasks in a fast-paced environment; ability to meet deadlines.
• Professional presence: strong interpersonal skills and professional demeanor.
• Education: Bachelor’s degree in a STEM field (e.g., Computer Science, Engineering, Statistics, Data Science).
• Experience: 2+ years of experience in data science, data engineering, or analytics.
• Modern data & AI engineering: 2+ years in data modeling, batch/streaming pipelines, structured/unstructured processing, and feature engineering/serving fundamentals.
• ML/LLM exposure: 1+ years with machine learning concepts, large language model applications, or AI-augmented analytics workflows.
• Programming & tooling: 2+ years using Python or R for statistical modeling and data wrangling, including proficiency in Databricks or Snowflake.
• Travel: ability to travel 0-25% on average.
• Work authorization: legally authorized to work in the United States without employer sponsorship, now or in the future.

Technologies

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

Team

• HC Forward is a dedicated innovation partner accelerating the future of Human Capital by building market-aligned products, platforms, and services that apply AI, data, and engineering to modernize HR experiences and outcomes.

Preferred

• Advanced degree (MS or PhD) and/or relevant certifications.
• Client-facing or consulting experience in Human Capital contexts and platforms/integrations (e.g., Workday, SAP SuccessFactors, Oracle HCM, Salesforce) or HR data domains.
• Experience with LLMOps and MLOps, including evaluation, monitoring, governance workflows, and model/prompt/version management.
• Cloud experience with AWS, Microsoft 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 product, data science/ML, data engineering, platform, and security teams.
• Experience building repeatable analytics products (tools/models/platforms) versus one-off deliverables, including ethics and privacy practices (consent, PII, and bias boundaries with appropriate escalation).

Location, Experience, and Pay

• Location: Indianapolis, IN (onsite)
• Experience level: 2+ years
• Salary range: USD 91,100 to 179,500 per year
• Wage range note: estimated range considers factors used to make compensation decisions.