Architect - Enterprise Application - SAP N 4D

Architect - Enterprise Application - SAP N

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Job Description

Job Purpose :

Data Platform Services is building a modern, governed, metadata-driven business data fabric powered by SAP Datasphere and the broader SAP Business Data Cloud (BDC) / SAP Business Technology Platform (BTP) ecosystem. This platform spans cross-system ingestion, semantic data layering, long-term persistence/archiving, and governed consumption across multiple downstream analytical and operational engines.

We are seeking a DataOps Engineer who treats spaces, data pipelines, replication flows, and semantic models as configuration, not one-off manual builds — someone who has designed or maintained environments where central metadata and rule tables drive how data assets are onboarded, transformed, partitioned, secured, and federated, rather than hand-crafting individual spaces and views from scratch.

You will operate across the full data lifecycle: hybrid ingestion/replication (landing → persisted business layer → cold store/archive), lifecycle and storage tiering strategies, and the dynamic routing engine that provides filtered, masked, and context-rich semantic views to multiple downstream consumers.

Key Responsibilities..

Build and operate resilient real-time and batch ingestion pipelines within SAP Datasphere using Replication Flows, Data Flows, and Transformation Flows from SAP (S/4HANA, ECC, BW/4HANA, SAP BDC) and non-SAP cloud/lake sources.

Design and maintain metadata-driven Space and Pipeline generation — where onboarding a new source entity, defining business semantics, or establishing delta replication is driven by configuration registries, templates, and CLI/API automation rather than manual UI workflows.

Implement retention and data tiering strategies using Datasphere cold storage integration, partition-based retention, and hybrid connectivity to external cloud object stores (e.g., ADLS Gen2, AWS S3, Snowflake).

Build and maintain a governed routing & semantic distribution engine: leverage a metadata rules registry (splittable, target_consumers[], row_filter_expr, column_mask_expr) to programmatically build and manage Data Access Controls (DAC), Analytic Models, and Consumption Views across Spaces.

Own Datasphere & BDC platform governance: architect Space hierarchies, user privileges, Space-to-Space sharing topologies, catalog registrations, and cross-system business context lineages.

Drive DataOps & CI/CD automation: manage automated asset lifecycle and deployment across environments (Dev/Test/Prod) leveraging Datasphere CLI, Content Network packages, REST APIs, and Git-integrated repositories.

Monitor platform health & replication telemetry: track replication lag, task execution logs, memory/storage consumption quotas across Spaces, and semantic view performance; build proactive alerting for SLA breaches and data drift.

Collaborate with SAP functional/data modeling teams to align on semantic consistency (CDS views, business context, ODP extractors) and translate consumer access requirements into secure, performant consumption models.

Document architecture, data flow patterns, and security topologies to maintain enterprise compliance and accelerate developer onboarding.

Required Skills & Experience

Core Platform & SAP Datasphere / BDC

Hands-on experience with SAP Datasphere (formerly Data Warehouse Cloud) and/or SAP BDC / BTP Data Services.

Strong hands-on expertise with Datasphere core artifacts: Spaces, Connections (DP Agent, Cloud Connectors), Replication Flows, Data Flows, Transformation Flows, View Modeling (Graphical & SQL/Script), and Analytic Models.

Deep understanding of SAP source integration patterns: CDS Views, ODP/Extractors, SAP Landscape Transformation (SLT), S/4HANA integrations, and federation vs. replication trade-offs.

Strong proficiency in SQL and SAP HANA Cloud engine concepts (calculation views, indexing, execution plan analysis, in-memory optimization).

Metadata-Driven Engineering & Automation (Critical)

Proven experience building or maintaining metadata/config-driven integration frameworks — where schemas, replication tasks, or security policies are programmatically driven by configuration tables or parameterized templates.

Experience with SAP Datasphere Command Line Interface (datasphere CLI), REST APIs, or Python scripts to automate tenant management, Space provisioning, CSN/JSON definition generation, and user onboarding.

Strong understanding of keeping semantic models and DAC rules synchronized with an authoritative source-of-truth metadata repository.

Platform Engineering / DataOps

CI/CD & Lifecycle Management: Hands-on experience with Datasphere Content Network, transport management, and version-controlled deployment pipelines (Azure DevOps, GitHub Actions, or GitLab CI).

Scripting & Orchestration: Strong scripting skills (Python, PowerShell, Bash) and experience with enterprise orchestrators (Airflow, Azure Data Factory, Control-M, or SAP BTP job scheduling).

Observability & Performance Tuning: Experience monitoring space storage quotas, replication queues, performance optimization of complex joined views, and setting up automated incident alerts.

Governance & Security

Deep expertise in Datasphere Data Access Controls (DAC), object-level authorizations, row-level filters, and column-level masking for sensitive/PII data.

Familiarity with enterprise cataloging and metadata lineage (SAP Datasphere Catalog, SAP Purview connector, Collibra, or Alation).

Strong written and verbal communication skills with the ability to bridge the gap between technical data engineering and business analytics teams.

Nice to Have

Experience with SAP Analytics Cloud (SAC) integration and Live Connection optimization.

Experience with SAP HANA Cloud / HDI Containers and native HANA database artifacts.

Hands-on knowledge of cloud lakehouse platforms (Databricks, Snowflake, Azure Synapse, Fabric) connected via Open Data Mesh / Federation to Datasphere.

Familiarity with SAP BTP global account and subaccount administration, Cloud Identity Services (IAS), and single sign-on (SSO).

What Success Looks Like in the First 6 Months

Config-Driven Asset Deployment: New entity ingestion and Space provisioning are automated via configuration tables and CLI scripts, minimizing manual UI modeling.

Automated Security & Access: Data Access Control (DAC) generation and Space-sharing policies operate seamlessly based on centralized security metadata.

Optimized Storage & Replication: Automated data tiering, delta replication monitoring, and partition pruning are established and operating reliably across production spaces.

End-to-End DataOps Pipeline: