Manager-Data Risk Management

Deloitte•Boston, MA

36d•$141,200 - $278,300•Hybrid

About The Position

Our Deloitte Regulatory, Risk & Forensic team helps client leaders translate multifaceted risk and an evolving regulatory environment into defensible actions that strengthen, protect, and transform their organization. Join our team and use advanced data, AI, and emerging technologies with industry insights to help clients bring clarity from complexity and accelerate their path to value creation. As a Manager in our AI & Data Risk team, you will lead data-driven transformation and risk-focused analytics engagements for clients across financial services, fintech, and technology industry sectors. You will be responsible for managing delivery teams, shaping solution approaches, and helping clients turn business needs into practical, scalable data and AI outcomes.

Requirements

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 mentor and provide clear guidance to others
Bachelor’s degree in Computer Science, Data Science, Information Technology, Information Systems, or a related field.
6+ years of experience in data analysis, data architecture, data management, or data engineering.
3+ years of experience in the financial services industry, including investment management or insurance.
Demonstrated proficiency in relevant tools and platforms, including: Data management: Collibra, Alation, Informatica, Atlan, Anomalo, Ataccama, or similar platforms. AI-assisted development and modern data engineering: Cursor, GitHub Copilot, Claude Code, dbt, Airflow, Git/GitHub, or similar tools. Predictive analytics: Python, R. Data visualization: Tableau, Power BI, or similar platforms. Data mining and data warehousing: Amazon Redshift, Google BigQuery, Databricks, Snowflake, or similar platforms. Programming and scripting: Python, SQL, Java, Scala, or similar languages.
Experience and familiarity with: Data modernization and migration projects in financial services. Data governance best practices, especially in the context of AI data readiness, data semantics, and ontologies. Presenting complex technical information to technical and non-technical stakeholders.
Ability to travel up to 50% on average, based on the work you do and the clients and industries/sectors you serve.
Limited immigration sponsorship may be available.

Nice To Haves

Master’s degree in Computer Science, Information Technology, Information Systems, or a business-related field.
Related certifications, such as PMP, AWS, Databricks, Azure, GCP Cloud Certification, or CDMP.

Responsibilities

Lead multidisciplinary teams to deliver high-quality data, analytics, and AI engagements, managing day-to-day execution, workplans, scope, timelines, risks, and dependencies.
Serve as product owner or delivery lead on assigned initiatives, shaping priorities and translating business needs into a clear roadmap and execution plan for AI and data engineering teams.
Manage, coach, and review the work of junior team members, providing direction, feedback, and support for skill development.
Drive modernization of business and technology processes in support of client objectives and project deliverables, including opportunities to apply agentic and automated workflows.
Partner with client business, data, and technology stakeholders to define requirements, align priorities, and support large-scale data and AI initiatives.
Lead data discovery and analysis efforts, including metadata curation, business glossary alignment, data lineage, classification, and source-to-target mapping, to support a strong data governance foundation.
Assess and improve data readiness for AI and agentic consumption, evaluating datasets for quality, completeness, and semantic requirements, and helping establish ontology components such as entities, relationships, and taxonomies.
Guide the design and build of AI-powered agents, accelerators, and reusable tooling to improve delivery speed, quality, and consistency across engagements.
Provide technical oversight across data analysis, data pipeline development, and solution reviews, helping teams deliver practical, reliable, and client-ready outputs.
Own project documentation, status reporting, issue tracking, and client-ready deliverables that clearly communicate progress, technical findings, risks, and recommendations.
Facilitate client workshops and present findings, insights, and recommendations to technical and non-technical stakeholders in a clear and compelling manner.
Build and sustain trusted relationships with client stakeholders and Deloitte team members, contributing to account growth and future engagement opportunities where appropriate.