BI Developer & Production Support Engineer

BI Developer & Production Support Engineer

The BI Developer & Production Support Engineer develops, enhances, and supports company's analytics products when citizen
development is not feasible or production accountability must remain internal. This job description applies to two positions
covering in-house development acceleration, production continuity, maintenance, and technical-debt reduction.
Mission
Accelerate priority BI delivery and keep cross-platform analytics and conversational BI reliable, maintainable, secure, cost-
aware, and well documented.
Key outcomes (what success looks like)
 High-quality dashboards and analytical applications delivered against certified data and acceptance criteria.
 Predictable production service through proactive monitoring, incident resolution, root-cause correction, and clear ownership.
 Reduced technical debt through refactoring, standardization, testing, documentation, automation, and disciplined releases.
 Successful transition of partner- and citizen-developed assets into governed production support.
Responsibilities
 Build accessible, executive-ready Power BI/Fabric dashboards, semantic models, reports, scorecards, and drill-through
experiences using reusable standards.
 Develop and troubleshoot DAX, Power Query, SQL, calculations, transformations, APIs, dataflows, pipelines, notebooks,
gateways, and deployment processes.
 Consume governed data from SAP BDC/Datasphere, Snowflake, Databricks, Fabric/Azure, enterprise APIs, and approved
operational sources without duplicating business logic.
 Support conversational BI metadata, trusted datasets, instructions, verified queries, permissions, and user experiences in
Databricks Genie, Snowflake Cortex, Microsoft Copilot-enabled analytics, and SAP analytical AI.
 Execute requirements traceability, prototyping, reconciliation, testing, UAT, accessibility checks, release readiness,
production validation, and knowledge transfer.
 Monitor freshness, semantic processing, warehouses/clusters, capacities, gateways, APIs, AI endpoints, audit logs,
incidents, releases, consumption, and dependency changes.
 Apply Git, peer review, CI/CD, environment promotion, automated testing, runbooks, incident/problem/change
management, and service reporting.
Required qualifications
 4–7+ years developing and supporting enterprise BI, analytics, or data products.
 Advanced Power BI, DAX, Power Query, SQL, semantic modeling, performance optimization, RLS, gateways, and
deployment-pipeline experience.
 Production experience with at least one modern data platform: Snowflake, Databricks, Microsoft Fabric/Azure, or SAP
BDC/Datasphere.
 Experience with requirements, technical design, testing, UAT, release, incident, problem, change, access, capacity,
consumption, and vendor-management processes.
 Working proficiency in Python, APIs, Git, CI/CD, monitoring, documentation, and cloud security concepts.
Preferred qualifications
 SAP S/4HANA and manufacturing/process-industry analytics.
 Snowflake Cortex, Databricks Genie, Copilot Studio, SAP Analytics Cloud/Joule, embedded analytics, or paginated
reporting.
 Experience accepting vendor-built solutions, supporting citizen development, and reducing technical debt in mixed-delivery
environments.
Expanded technology requirements
 Cross-platform architecture across Microsoft Fabric and Power BI, Snowflake, Databricks, and SAP Business Data Cloud,
including workload placement, interoperability, semantic consistency, identity, networking, data movement, resiliency, and
total-cost tradeoffs.
 SAP Business Data Cloud ecosystem: SAP Datasphere, SAP Analytics Cloud, SAP Databricks, SAP HANA Cloud, SAP
Master Data Governance, SAP BW modernization, curated business data products, and governed third-party connectivity.
 Snowflake ecosystem: architecture and virtual warehouses, RBAC, secure sharing, Snowpark, Streamlit, Cortex AI
Functions, Cortex Analyst, Cortex Search, Cortex Agents, embeddings/vector patterns, observability, and consumption
controls.
 Databricks ecosystem: lakehouse and medallion architectures, Delta Lake, Unity Catalog, Lakeflow, SQL Warehouses,
notebooks, MLflow, Model Serving, vector search, AI/BI dashboards, Genie Agents, and governed business semantics.
 Microsoft ecosystem: Power BI and Fabric semantic models, Direct Lake, OneLake, lakehouse/warehouse, pipelines,
notebooks, Copilot Studio, Azure AI services, APIs, deployment pipelines, monitoring, and capacity management.
 Engineering and governance: advanced SQL, Python, REST APIs, OAuth/service principals, secrets management, Git and
CI/CD, automated testing, metadata, lineage, data quality, FinOps, privacy, cybersecurity, and Responsible AI controls.
 Platform-neutral design using approved open formats, APIs, reusable data contracts, portable business definitions, and
documented integration boundaries to limit avoidable lock-in.
 Operational depth across Power BI/Fabric, Snowflake warehouses and Cortex services, Databricks SQL/compute and
Genie, SAP BDC/Datasphere/SAC, APIs, gateways, identities, schedules, and monitoring.
 Performance and cost optimization across DAX/semantic models, SQL queries, data refresh, warehouses, clusters,
capacity, storage, network movement, and AI inference consumption.
Core competencies
 Delivery orientation and practical problem solving.
 Operational ownership and root-cause discipline.
 Data storytelling for operational and executive audiences.
 Engineering quality, reuse, testing, security, and maintainability.
 Effective collaboration with business owners, partners, architects, and citizen developers.