Data Engineer II

Deloitte is hiring a Data Engineer II to design and deliver data engineering and analytics solutions for workforce and human capital challenges. The role builds scalable data pipelines, dashboards, and reusable analytics products, and supports AI, machine learning, and generative AI capabilities with trusted, governed data foundations.

Responsibilities

Lead the design, development, and delivery of analytics solutions that address complex workforce and human capital challenges for clients across industries

Build and maintain scalable data pipelines, dashboards, and reporting frameworks using Structured Query Language (SQL) and Tableau (or equivalent visualization platforms)

Translate ambiguous business problems into structured analytical approaches, communicating findings and recommendations clearly 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 efforts including proposals, client presentations, and thought leadership content

Design and deliver intuitive, executive-ready reports and dashboards that make complex workforce data accessible and actionable

Preferred

Advanced degree, including a Master of Science (MS) or Doctor of Philosophy (PhD), and/or relevant certifications

Direct experience in client-facing or consulting environments, with Human Capital platforms and integrations (e.g., Workday, SAP SuccessFactors, Oracle Human Capital Management (HCM), Salesforce), or in Human Resources (HR) data domains

Experience operationalizing large language model operations (LLMOps) and machine learning operations (MLOps) capabilities, including evaluation, monitoring, governance workflows, and model/prompt/version management

Cloud experience on one or more of Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (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 in analytics product development—building repeatable tools, models, or platforms rather than one-off deliverables—and ethics and privacy practices, including recognizing consent, personally identifiable information (PII), and bias boundaries and escalating appropriately

Advanced Degree

Relevant Certifications

Client-Facing Consulting

Workday

SAP SuccessFactors

Oracle HCM

Salesforce

Human Capital Platforms

HR Data Domains

LLMOps

MLOps

Model Evaluation

Model Monitoring

Governance Workflows

Model Management

Prompt Management

Version Management

AWS

Microsoft Azure

Google Cloud Platform

Managed Data Platforms

Scalable Compute Patterns

Analytics Product Development

Repeatable Tool Development

Model Development

Platform Development

AI Ethics

Data Privacy

Consent Practices

PII Recognition

Bias Recognition

Complex Concept Communication

Risk

Trade-Off Communication

Decision Documentation

Cross-Functional Collaboration

Ethical Judgment

Appropriate Escalation

Benefits

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Opportunities to build new skills, take on leadership opportunities and connect and grow through mentorship.

On-the-job learning experiences and formal development programs.

Company

Audit, tax, consulting, and financial advisory services

London, England, GBR

https://www2.deloitte.com/

Founded in 1845

10001+ employees

Qualifications

Bachelor's degree in a Science, Technology, Engineering, and Mathematics (STEM) field (e.g., Computer Science, Engineering, Statistics, Data Science)

2+ years of experience in data science, data engineering, or analytics

2+ years of modern data & AI engineering, including data modeling, batch/streaming pipelines, structured/unstructured processing, and feature engineering/serving fundamentals

1+ years of experience with machine learning concepts, large language model applications, or AI-augmented analytics workflows

2+ years of experience using Python or R for statistical modeling and data wrangling, including proficiency in Databricks or Snowflake

Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future

Ability to work independently and collaborate as part of a team

Effective written and verbal communication skills

Meticulous attention to detail and quality of work product

Ability to build and sustain professional relationships

Ability to lead projects or workstreams

Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment

Strong interpersonal skills and professional demeanor

Ability to meet deadlines

Ability to provide clear guidance to others

Advanced degree, including a Master of Science (MS) or Doctor of Philosophy (PhD), and/or relevant certifications

Direct experience in client-facing or consulting environments, with Human Capital platforms and integrations (e.g., Workday, SAP SuccessFactors, Oracle Human Capital Management (HCM), Salesforce), or in Human Resources (HR) data domains

Experience operationalizing large language model operations (LLMOps) and machine learning operations (MLOps) capabilities, including evaluation, monitoring, governance workflows, and model/prompt/version management

Cloud experience on one or more of Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (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 in analytics product development—building repeatable tools, models, or platforms rather than one-off deliverables—and ethics and privacy practices, including recognizing consent, personally identifiable information (PII), and bias boundaries and escalating appropriately

2+ years of experience in data science, data engineering, or analytics.

2+ years of modern data & AI engineering, including data modeling, batch/streaming pipelines, structured/unstructured processing, and feature engineering/serving fundamentals.

1+ years of experience with machine learning concepts, large language model applications, or AI-augmented analytics workflows.

2+ years of experience using Python or R for statistical modeling and data wrangling, including proficiency in Databricks or Snowflake.

Required skills

Data Engineering, Data Modeling, Batch and Streaming Pipelines, Structured and Unstructured Data Processing, Feature Engineering and Serving, Python, R, Databricks, Snowflake, Machine Learning, Large Language Model Applications, LLMOps and MLOps

Experience

2+ years