Sr Analytics Engineer

Your Responsibilities The Senior Analytics Engineer is responsible for designing, developing, governing, and maintaining Finance-domain data assets used across the organization. This role owns key Gold-layer data objects and the semantic models that support financial and operational reporting products and analytics. The position works closely with the Digital Data Product and Data Engineering teams to ensure alignment with enterprise data standards, coordinate integration with enterprise data pipelines, and support production data processes. The engineer also serves as a bridge between business stakeholders and technical partners, translating business requirements into scalable data models and transformation logic. Key Responsibilities Gold Layer & Semantic Model Ownership 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. Analytics & Business Translation 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. Transformation Development (Databricks) 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. Collaboration with Digital Data & Engineering Teams 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. Source Migrations & MDM Integration 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. Automation, Orchestration & Workflow Enablement 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. Documentation & Data Governance 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. Version Control & Deployment Support 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 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 Technical Experience 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. #J-18808-Ljbffr Lockton