Enterprise Semantic Layer & Ontology Engineer

Sapta Global Inc.; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

Sapta Global Inc. seeks an Enterprise Semantic Layer & Ontology Engineer to build a governed analytics foundation across SAP and non-SAP domains.

You will translate business definitions into reusable data products, certified metrics, and semantic models for executive reporting, self-service analytics, AI, and conversational BI. Role focuses on cross-platform semantic engineering, secure data contracts, and scalable data products across Snowflake, Databricks, Fabric/Azure, and SAP Datasphere,

Qualifications

• 5–8+ years in data engineering, analytics engineering, data warehousing, or related field.
• Advanced SQL and dimensional modeling with semantic layers and reusable metrics.
• Hands-on experience with Snowflake, Databricks, Fabric/Azure, or SAP Datasphere.
• Experience with SAP S/4HANA data, security, privacy, governance, and releases.
• Experience operationalizing governed data products across environments.

Responsibilities

• Design dimensional, relational, lakehouse, data vaults and semantic models for multiple domains.
• Translate business rules into reusable measures, hierarchies, conformed dimensions and datasets.
• Engineer data pipelines across SAP S/4HANA, Snowflake, Databricks, Fabric/Azure, SQL, and APIs.
• Choose federation, virtualization, or persistence based on latency, scale, and governance.
• Implement reconciliation, observability, lineage, metadata, and production runbooks.
• Collaborate with governance, MDM, BI, AI, platform, cybersecurity, and business owners.

Skills

SQL proficiency

Semantic modeling

Cross-platform data engineering

Data governance & security

Tools

Snowflake

Databricks

Microsoft Fabric/Azure

SAP Datasphere

Job description

Enterprise Semantic Layer & Ontology Engineer

Enterprise Semantic Layer & Ontology Engineer builds and operates company's governed analytics foundation across SAP and non-SAP domains. The role translates business definitions into reusable data products, certified metrics, semantic models, and reliable integration patterns for executive reporting, self-service analytics, AI, and conversational BI.

Mission

Create a durable cross-platform semantic and data-product layer that standardizes critical KPIs, reduces duplicative engineering, and provides trusted business context to analytics and AI.

Key outcomes (what success looks like)

• Certified semantic models and data products with ownership, definitions, lineage, quality controls, and row-level security.
• Consistent KPI calculations across Power BI, Snowflake Cortex, Databricks Genie, SAP analytics, and downstream applications.
• Reliable batch, streaming, CDC, federation, and sharing patterns meeting service, performance, security, and cost expectations.
• Reduced direct source-system extraction and duplicate transformations through governed reuse.

Responsibilities

• Design dimensional, relational, lakehouse, data-vault where appropriate, and semantic models for Finance, Operations, Supply Chain, HSE, Commercial, and corporate domains.
• Translate approved business rules into reusable measures, hierarchies, conformed dimensions, data contracts, synonyms, descriptions, verified queries, and certified datasets.
• Engineer pipelines and data products across SAP S/4HANA, SAP BDC/Datasphere, Snowflake, Databricks, Microsoft Fabric/Azure, SQL, APIs, and approved operational or historian sources.
• Select federation, virtualization, zero-copy sharing, replication, or persistence based on latency, scale, reliability, governance, operability, and cost.
• Implement reconciliation, observability, restartability, performance tuning, access controls, lineage, metadata, release management, and production runbooks.
• Partner with governance, MDM, BI, AI, platform, cybersecurity, vendors, and business owners through architecture review and production acceptance.

Required qualifications

• 5–8+ years in data engineering, analytics engineering, data warehousing, enterprise BI, or data-platform engineering.
• Advanced SQL and strong experience in dimensional modeling, semantic layers, reusable metrics, pipeline orchestration, testing, monitoring, and production support.
• Hands‑on expertise in at least one major platform: Snowflake, Databricks, Microsoft Fabric/Azure, or SAP Business Data Cloud/Datasphere, with demonstrated cross-platform integration ability.
• Experience with SAP S/4HANA business data and enterprise security, identity, privacy, governance, and release practices.
• Experience operationalizing governed data products across development, test, and production environments.

Preferred qualifications

• Manufacturing/process‑industry and OT/historian experience.
• SAP BW modernization, SAP BDC data products, Snowflake secure sharing, Databricks Unity Catalog, and Fabric Direct Lake experience.
• Cloud architecture, infrastructure/configuration as code, master data management, cataloging, and FinOps experience.

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.

Semantic engineering across Power BI, Databricks Genie, Snowflake Cortex Analyst, and SAP analytics, including metric definitions, verified questions, business rules, descriptions, synonyms, and authorization‑aware context.

Data engineering patterns for structured, semi‑structured, unstructured, time‑series, document, and multimodal workloads.

Core competencies

• Systems thinking and platform-neutral architecture.
• Analytical rigor and reconciliation discipline.
• Engineering quality, documentation, and production ownership.
• Clear communication across business, governance, security, and technology stakeholders.