Manager, Data Engineer - PySpark

KPMG LLP; Dallas (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

KPMG LLP seeks a Manager, Data Science Engineer to lead analytics initiatives across consulting domains, focusing on NLP, ML, and data engineering. You will guide multi-disciplinary teams, translate complex analytics problems into actionable approaches, and drive end-to-end delivery from data discovery to deployment.

The role requires leadership experience, strong technical depth in Python, PySpark, SQL, and cloud platforms (AWS/GCP/Azure), and a proven ability to deliver measurable business

Qualifications

• Minimum five years leading teams of data scientists, engineers, and analytics professionals.
• Experience delivering end-to-end analytics and data engineering solutions including discovery, cleansing, model development, validation, deployment, and MLOps.
• Ability to apply AI techniques to concrete business goals and work with business to understand data resources and constraints.

Responsibilities

• Translate analytics problems into technical approaches and communicate results to stakeholders across industries.
• Plan engagement objectives, deliverables, and milestones, and manage data discovery, retrieval, and processing from diverse sources.
• Collaborate with Tax, Audit, and Advisory to advance data science understanding and integrate AI solutions.
• Lead multi-disciplinary teams to rapidly iterate models and communicate results to executives.
• Provide oversight for building machine learning algorithms and data science solutions, including code quality and data preparation.
• Act with integrity and uphold a respectful work environment.

Skills

Python

PySpark

SQL

Databricks

Snowflake

Machine Learning

NLP

Data Engineering

R

Education

Master's degree in Computer Science/Statistics/Data Science/Engineering

PhD preferred

Tools

AWS

GCP

Azure

Job description

KPMG is currently seeking a Manager, Data Science Engineer to join our Consulting practice.

Responsibilities:

• Translate advanced business analytics problems into technical approaches that yield actionable recommendations, in diverse domains. Communicate technical details of solution, including mathematical formulations, alternatives, and impact on modeling approach to business stakeholders across industries (technology, financial services, emerging tech, government agencies - federal, state and local, and utilities).
• Plan engagement objectives, key deliverables, and deliver on engagement milestones by following analytics processes; Work with clients to discover, retrieve, prepare, and process a variety of data sources (social media, news, internal/external documents, emails, financial, and operational).
• Work internally within KPMG's other areas within Tax, Audit and Advisory to advance the client understanding of Data Science, Artificial Intelligence, and Advanced Analytics; Work very closely with emerging technologies group within AI service providers (e.g., AWS, GCP, Azure) to provide a first-in-market solution approaches to complex business problems.
• Lead multi-disciplinary and cross-functional teams to rapidly iterate models and results to refine and validate approach; Lead team in a fast-paced and dynamic environment with both virtual and face-to-face interactions; Utilize structured approaches to solving problems, managing risks, and multiple responsibilities using structured approaches for operational excellence; Communicate results to executive level audiences.
• Leverage deep technical knowledge to build, review, and quality control code to prepare, extract, and enrich data sources, working with the business to understand available resources and constraints around data. Lead team in exploratory data analysis, generating and testing working hypotheses, and uncovering interesting trends and relationships. Provide expert oversight for teams building machine learning algorithms and data science solutions.
• Act with integrity, professionalism, and personal responsibility to uphold KPMG’s respectful and courteous work environment

Qualifications:

• Minimum of five years of experience leading teams of at least five data scientists, engineers, and other data & analytics professionals, including business development, requirements gathering, people development, and quality management using analytics and software development processes for natural language processing, machine learning on unstructured data, and/or information retrieval; Multidisciplinary backgrounds.
• Master's degree from an accredited college/university in Computer Science, Statistics, Data Science, Engineering, or related fields; PhD from an accredited college/university is preferred
• Proven experience delivering end-to-end analytics and data engineering solutions, including data discovery, cleansing, model development, validation, deployment, and MLOps, with hands‑on expertise in Python, PySpark, SQL, Databricks, and Snowflake, while partnering with clients and cross‑functional teams to solve complex business challenges and drive measurable outcomes.
• Ability to apply artificial intelligence techniques to achieve concrete business goals; ability to work with the business to understand available resources and constraints around data (sources, integrity, and definitions), processing platforms, and security; Provide assistance, and resolve problems, using solid problem‑solving skills and strong verbal/written communication.
• Ability to utilize a diverse array of technologies and tools as needed, to deliver insights, such as fluency in Python or R; Experience with SQL and cloud technology preferred; Experience with command‑line scripting, data structures, and algorithms; Ability to work in Linux and/or cloud environments; Ability to write production level code.
• Ability to travel
• Must be authorized to work in the U.S. without the need for employment‑based visa sponsorship now or in the future. KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H‑1B, L‑1, TN, O‑1, E‑3, H‑1B1, F‑1, J‑1, OPT, CPT or any other employment‑based visa)