Senior Software Engineer

In this role, you will: Design, develop, and maintain scalable data pipelines, transformations, and models that convert complex operational and business data into trusted datasets. Ingest and curate data across support and field-service domains, including incidents, tickets, chats, problem management, quality analytics, user and inventory information, and customer survey data. Design dimensional models and reusable semantic models with consistent definitions, relationships, hierarchies, and business logic. Establish data products that support reporting, analytics, operational insights, automation, and downstream natural-language data experiences. Create scalable processes for onboarding new data fields without requiring extensive downstream report and measure changes. Implement automated data-quality controls for completeness, accuracy, freshness, schema changes, referential integrity, and business-rule validation. Develop and improve metadata-management capabilities, including technical descriptions, business definitions, data lineage, ownership, and discoverability. Partner with business stakeholders, product managers, program managers, engineers, and data consumers to translate requirements into scalable technical solutions. Define stable, secure, and well-documented data contracts for BI, analytics, automation, and downstream application teams. Implement security and governance controls, including role-based access, data privacy, retention, auditing, and appropriate access to sensitive information. Contribute to deployment automation, CI/CD pipelines, source-control practices, testing frameworks, and operational tooling. Support live-site operations by investigating data issues, responding to incidents, implementing corrective actions, and reducing recurring failures. Build monitoring and observability for pipelines, lakehouses, semantic models, refreshes, data quality, and service-level objectives. Analyze report, measure, and column usage to identify redundant or underused assets and support report-portfolio rationalization. Collaborate across teams to share goodpractices, improve engineering standards, evaluate emerging technologies, and drive adoption of modern data engineering capabilities. SQL/Advanced SQL, Python, PySpark, ETL/ELT, Data Modeling, Data Transformation, Incremental Processing, Error Handling, Scalability, Edge Cases, Performance Optimization. Experience designing, developing, or operating production data pipelines and ETL/ELT processes. Experience implementing CI/CD and automated deployment processes for cloud data platforms. Experience implementing data validation, testing, monitoring, and operational support for production data solutions. Experience translating business requirements into scalable technical designs and data products. Familiarity with preparing curated, documented data for downstream natural-language analytics or AI-assisted applications. Experience developing and maintaining Power BI semantic models, including DAX, shared business definitions, and model optimization. Knowledge of Delta Lake, medallion architecture, schema evolution, and reusable data-product or data-contract patterns. Experience supporting business-critical data platforms through live-site incident response, root-cause analysis, and resiliency improvements. Experience modernizing or rationalizing a large reporting and analytics estate. Familiarity with enterprise support, customer experience, service management, finance, or field-service data.