Manager II, Data Engineering

Kroll

65d • $90,000 - $200,000

About The Position

At Kroll, our professionals combine analytical rigor, deep market insight, and independence to deliver defensible analysis and practical solutions. We think globally, act with transparency, and encourage our people to do the same. Join our collaborative and supportive environment, where your work makes a real impact, this is the Kroll difference. We are strengthening our data engineering capabilities to transform how we deliver internal business intelligence and external client reporting. As a Lead Data Engineer, you will take ownership of complex initiatives, guide best practices, and mentor other engineers — while remaining deeply hands-on. This role will report to the Portfolio Valuation service line, supporting their strategic data initiatives and client delivery goals. At Kroll, your work helps protect, restore, and maximize value for our clients. Join us—and together, we’ll maximize the value of your career.

Requirements

• Bachelor’s degree in computer science, Engineering, or related field (master’s preferred). Equivalent experience considered.
• 5+ years of experience in data engineering or software engineering, with at least 2 years in a senior or lead role.
• Proven experience building and managing ETL/ELT pipelines
• Advanced proficiency with Azure, AWS, and Databricks (with a focus on data services)
• Deep knowledge of Python, Spark ecosystem (PySpark, Spark SQL), and relational databases
• Experience building REST APIs, Python SDKs, libraries, Spark-based data services and tools like FastAPI, Pydantic, Polars, Pandas, Delta Lake, Docker, Kubernetes
• Understanding of Lakehouse architecture, Medallion architecture, and data governance
• Experience with pipeline orchestration tools (e.g., Airflow, Azure Data Factory)
• Strong communication skills and ability to work cross-functionally with international teams
• Skilled in data profiling, cataloging, and mapping for technical data flows
• Understanding of API product management principles, including lifecycle strategy, documentation standards, and versioning
• Experience with CI/CD, Git, and DevOps best practices

Nice To Haves

• Familiarity with financial statements and valuation methodologies (income approach, market approach, etc.).
• Broader cloud architecture awareness (networking, security, cost optimization).
• Experience tuning complex SQL/Spark queries and pipelines for performance.
• Hands-on experience building Lakehouse solutions using Azure Databricks, ADLS, and/or Snowflake as part of a modern data platform architecture.

Responsibilities

• Design and build scalable, reliable data infrastructure and pipelines.
• Mentor junior and mid-level engineers through code reviews, coaching, and knowledge sharing.
• Identify and implement process improvements (automation, optimization, monitoring).
• Collaborate with valuation professionals to understand the business needs and requirements.
• Lead complex technical implementation work, ensuring performance and quality standards.
• Collaborate with global teams to deliver fault-tolerant, high-quality data solutions.
• Drive engineering excellence by enforcing coding standards, automated unit testing, and CI/CD best practices.
• Lead and participate in global trainings to enhance the understanding of the infrastructure by valuation professions.
• Contribute to architecture discussions and proof-of-concepts, providing input without direct ownership of roadmap.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Bachelor's degree
• Number of Employees: 5,001-10,000 employees