59 Lockton Operations Jobs Hiring Near You

Job Summary:
Lockton is a company focused on data-driven solutions, and they are seeking a Senior Analytics Engineer responsible for designing and maintaining Finance-domain data assets. This role involves collaboration with various teams to ensure data accuracy, develop transformation logic, and support financial reporting products.
Responsibilities:
• Own and maintain key Finance Gold-layer tables and transformations that support financial and operational reporting.
• Manage and enhance the Finance semantic models, including metric definitions, dimensional structures, and relationships used across reporting products.
• Ensure consistency in modeling standards, naming conventions, and data definitions across Finance and shared enterprise objects in collaboration with the Digital Data Product team.
• Maintain alignment between curated Gold datasets and downstream reporting models.
• Own Finance-specific automated workflows from design through production support and ongoing enhancement.
• Work with Finance, Accounting, Operations, and other business groups to translate business needs into technical data models, transformation logic, and reporting structures.
• Perform exploratory and validation analysis to clarify business rules, confirm assumptions, and refine transformation logic.
• Serve as a subject matter expert on Finance data lineage and how data flows from Source → Bronze → Silver → Gold → reporting and analytics layers.
• Develop and maintain transformation logic using Databricks notebooks.
• Implement data validation and monitoring to ensure data accuracy and reliability.
• Contribute to tools or dashboards that support ongoing data quality monitoring and operational visibility.
• Provide specifications, logic requirements, and acceptance criteria for integrating Finance transformations into enterprise data pipelines.
• Partner with Digital Data Engineering teams on pipeline orchestration, quality controls, monitoring, incident resolution, and production support.
• Participate in design discussions regarding upstream data changes that impact Finance datasets.
• Support the integration of new MDM and Accounting data sources into Finance reporting models.
• Define mapping logic, update transformations, and assist with historical data realignment to support new dimensional structures.
• Assess downstream impacts and support validation and testing during migration and cutover activities.
• Design and define automated workflows connecting Databricks transformations, pipelines, and Power BI semantic models, ensuring alignment with Finance reporting requirements.
• Develop Databricks notebooks and Jobs that support operational processes such as post-pipeline validation, data readiness checks, and downstream refresh triggers.
• Evaluate automation approaches (Databricks Jobs, Power Automate, APIs, scheduled pipelines, etc.) and recommend solutions that ensure reliability, maintainability, and alignment with platform standards.
• Contribute to monitoring, logging, and error-handling patterns that ensure automated processes are observable and supportable.
• Maintain clear documentation of business logic, transformation rules, metric definitions, and data lineage for Finance-owned datasets.
• Support governance standards related to modeling practices, naming conventions, and data definitions across Finance data assets.
• Partner with governance and security teams to ensure Finance data models support appropriate access controls and sensitivity classifications.
• Use Git for code versioning, pull requests, and peer reviews.
• Collaborate with Digital teams on CI/CD processes and deployment of Databricks assets, including notebooks, jobs, and logic updates.
Qualifications:
Required:
• 7+ years of experience in Analytics Engineering, Data Engineering, or similar data-focused roles.
• Strong experience with SQL and Python for building transformation logic and analytical workflows.
• Experience working with curated data models, semantic layers, or Gold-layer data assets.
• Strong understanding of dimensional modeling, data lineage, and metric definition.
• Ability to translate business requirements into structured data models and transformation logic.
• Strong analytical and problem-solving skills with the ability to understand business context.
Preferred:
• Experience working with Databricks notebooks, Jobs, and Spark-based data transformations.
• Experience integrating Databricks data models with Power BI semantic models.
• Familiarity with orchestration and automation tools such as Databricks Jobs, Power Automate, APIs, or similar platforms.
• Experience designing end-to-end analytics workflows spanning data pipelines, models, and reporting layers.
• Ability to evaluate automation and orchestration approaches based on complexity, reliability, and scalability.
• Experience optimizing data models and transformations for performance and cost efficiency within Databricks and downstream BI tools.
• Basic familiarity with Microsoft Fabric Lakehouse and Warehouse components, including Databricks mirroring for analytics and reporting use cases.
Company:
Lockton provides risk management and insurance services. Founded in 1966, the company is headquartered in Kansas City, USA, with a team of 10001+ employees. The company is currently Late Stage.