Data Engineer

About

At KPMG, you’ll join a team of diverse and dedicated problem solvers, connected by a common cause: turning insight into opportunity for clients and communities around the world.

Are you a talented leader with a proven track record for motivating teams and delivering exceptional client service?

Our team is looking for a Data Engineer with extensive hands‑on expertise in Databricks and strong consulting capability. This role will support and lead modernization initiatives from legacy/on‑prem data platforms to scalable, secure, and cost‑optimized Lakehouse architectures using Databricks and similar technologies.

Responsibilities

• Partner with clients to understand business goals, gather requirements, and translate them into actionable technical designs and delivery plans.
• Work with the engagement team to translate business and analytics requirements into a data strategy for the engagement including ETL/ELT, data model, and staging data for analysis.
• Contribute to end‑to‑end solution architecture for repeatable, cost‑optimized implementations (including non‑functional requirements and operational readiness).
• Lead delivery of modern data platforms on Databricks (ETL/ELT pipelines, workload migrations, governance enablement).
• Implement Delta Lake / Lakehouse patterns including medallion architecture, CDC, incremental processing, and data quality controls.
• Develop data pipelines to support streaming, incremental, batch data, etc.
• Design and implement scalable batch and streaming pipelines using Spark and modern orchestration patterns.
• Apply CI/CD and engineering best practices (version control, automated deployment, testing, and release management) to data engineering workflows.
• Establish and operationalize governance using Unity Catalog, including access controls, lineage, and security frameworks.
• Support testing and production releases, including troubleshooting, performance tuning, and stabilization.
• Proactively contribute to the creation of presentation materials relating to data activities for stakeholder discussions.

Requirements

• University degree in computer engineering, mathematics, data science or related disciplines
• 4+ years of professional experience in a related field like Data Engineering, Business Intelligence, or related field with a track record of manipulating, processing, and extracting value from large datasets.
• 2+ years of hands‑on experience with Databricks, including advanced features (Delta Lake, Unity Catalog) or cloud certifications, with 1‑2 years of experience leading workstreams / client‑facing delivery.
• Strong proficiency in SQL and solid understanding of modern data modeling principles, dimensional modeling, and data warehousing concepts.
• Proficiency in Python (or similar scripting languages) for data processing, automation, and analytical workflows.
• Strong experience working in teams to perform ETL (extract, transform and load) of data from a variety of databases (SQL, NoSQL, etc.).
• Proven experience leading large‑scale data migrations (ETL, workloads, cloud platforms), including migration of legacy data platforms or ETL workloads to cloud‑native environments.
• Experience applying CI/CD practices to data engineering workflows, including version control, automated deployment, and pipeline orchestration.
• Independent ability to review data quality and data definitions and perform data cleansing and data management tasks.
• Experience collaborating within cross‑functional and multi‑disciplinary teams to solve complex data challenges, including processing semi‑structured and unstructured data.
• Experience in at least one major cloud service (AWS, Azure, or GCP) with understanding of cloud‑native services, identity management, and scalable architecture principles.
• Certifications: Databricks Certified Data Engineer (Associate or Professional) and/or relevant cloud certifications (e.g., Azure, AWS, or GCP architecture or data engineering credentials) are preferred.