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Kroll

Global risk management and investigations consulting

Senior Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
  • At least 5 years of experience in Data Engineering, Data Architecture, Platform Engineering, or related disciplines.
  • Extensive experience designing enterprise-scale cloud data platforms.
  • Strong understanding of modern data architecture principles, including Lakehouse, Data Mesh, and Data Fabric concepts.
  • Experience implementing enterprise data governance and security frameworks.
  • Proven ability to lead large-scale technical initiatives from design through production.
  • Experience with Microsoft Azure, AWS, Databricks, Microsoft Fabric, Snowflake, Azure Synapse Analytics, Azure Data Lake Storage, SQL Server, Oracle, PostgreSQL, Azure SQL, Delta Lake, Spark, PySpark, SQL, Python, ETL/ELT pipeline development, streaming and batch processing, Microsoft Purview, Unity Catalog, data catalogs, data lineage, data classification, data masking, data quality frameworks, Git, Azure DevOps, GitHub Actions, Terraform, ARM/Bicep, CI/CD pipelines, Power BI, semantic models, DAX, and data modeling.

Responsibilities

  • Design and maintain enterprise data architecture aligned with business strategy and technology roadmaps.
  • Define logical, conceptual, and physical data models across operational and analytical platforms.
  • Develop scalable architecture patterns for data lakes, data warehouses, lakehouses, and modern analytics platforms.
  • Establish standards for metadata, lineage, master data, reference data, and data lifecycle management.
  • Drive technology evaluations and architectural decisions for cloud-based data platforms.
  • Design, build, and optimize enterprise data platforms using cloud-native technologies.
  • Develop and maintain scalable ETL/ELT pipelines for structured and semi-structured data.
  • Implement infrastructure automation and deployment pipelines using Infrastructure as Code and CI/CD practices.
  • Optimize platform performance, reliability, scalability, and cost across cloud environments.
  • Troubleshoot platform issues and continuously improve operational resilience.
  • Design and implement enterprise data governance frameworks.
  • Lead initiatives involving confidential data identification, classification, masking, tokenization, encryption, and access control.
  • Define and implement data retention, auditing, and compliance standards.
  • Collaborate with Security, Risk, Legal, and Compliance teams to ensure adherence to regulatory requirements.
  • Establish enterprise metadata management and data lineage capabilities.
  • Develop enterprise data quality frameworks, including validation, reconciliation, completeness, accuracy, and monitoring.
  • Implement automated quality controls within ingestion and transformation pipelines.
  • Define KPIs and metrics to measure platform health and data quality.
  • Lead initiatives that improve trust in enterprise data assets.
  • Administer enterprise analytics platforms, including Databricks, Microsoft Fabric, Snowflake, and Azure and AWS environments.
  • Manage workspace provisioning, Unity Catalog governance, compute optimization, storage management, and platform security.
  • Implement role-based access controls and least-privilege security models.
  • Monitor platform utilization, capacity planning, and cost optimization.
  • Partner with BI teams to enable trusted enterprise reporting.
  • Support semantic models, reporting platforms, and self-service analytics capabilities.
  • Ensure data products are discoverable, reusable, and governed.
  • Lead architecture reviews and technical design sessions.
  • Provide technical leadership to engineering teams and mentor junior architects and engineers.
  • Define enterprise best practices, standards, and reusable design patterns.
  • Review solution designs for scalability, maintainability, security, and performance.
  • Improve platform reliability through monitoring, automation, observability, and incident response.
  • Develop operational runbooks and disaster recovery strategies.
  • Drive continuous improvement initiatives across platform operations.
  • Support production deployments and release management.
  • Partner with Finance, HR, Operations, Product, Security, and Engineering teams to understand business requirements.
  • Translate business objectives into scalable technical solutions.
  • Communicate architecture decisions and technical strategy to technical and executive audiences.
  • Lead cross-functional initiatives involving multiple business units and external vendors.

About the Company

Kroll

Global risk management and investigations consulting

Preparing summary

Company size
5,001-10,000
Company stage
Not listed
Total funding
Not listed
Headquarters
New York City, New York
Founded
1932

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