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 governed, metadata-driven data integration and staging platform centered on SAP BusinessObjects Data Services (BODS) and enterprise databases/data lakes — spanning source ingestion (SAP ERP/S/4HANA, non-SAP systems), a source-faithful central staging repository, long-term archival, and controlled distribution to multiple downstream systems.

We're looking for a DataOps Engineer who treats ETL pipeline design, data flows, and access-control logic as configuration, not one-off code — someone who builds and maintains systems where metadata tables and control frameworks drive what gets executed (jobs, interfaces, delta loads, transformations, and security masking), rather than hand-crafting individual one-off BODS jobs for every new dataset or consumer.

You'll work across ingestion (landing → central staging → archive), retention/partitioning strategy, and the routing/distribution rules engine that determines how data is extracted, transformed, filtered, or masked across multiple major downstream systems.

What You'll Do

Build and operate robust batch and incremental change data capture (CDC) ingestion pipelines using SAP Data Services (BODS) from SAP (ECC, S/4HANA, SLT, ODP/Extractors) and non-SAP sources into central staging repositories/databases (e.g., SQL Server, Oracle, SAP HANA, or Cloud Storage).

Design and maintain metadata-driven ETL generation & execution frameworks — where dataset onboarding (new source table, delta logic, schema mapping, validation rules) is driven by configuration tables and reusable BODS templates/scripts rather than creating bespoke data flows from scratch.

Implement and maintain partition-based retention, database archiving, and extraction routines for historical data and rolling window retention policies (e.g., current + N prior years).

Build and maintain a dataset routing-rules engine: a governed metadata table (splittable, target_systems[], row_filter_expr, column_mask_expr) that programmatically directs and transforms BODS distribution jobs and target views per downstream system.

Own BODS repository architecture & governance: manage local/central repositories, Data Services Management Console (DSMC), user access security, environment promotion (Dev/Test/Prod), and execution auditing/lineage.

Drive DataOps & CI/CD automation: streamline BODS export/import/transport automation, repository synchronization, and job scheduling via Enterprise Scheduling tools (e.g., Autosys, Control-M, Airflow, or Azure Data Factory).

Monitor pipeline health, job execution logs, CDC checkpoint integrity, schema drift, and data SLAs; establish automated alerting and self-healing for job failures and data-freshness breaches.

Collaborate with upstream SAP functional/basis teams to define data contracts (e.g., Delta queue/ODP parameters, timestamps, extraction load impact) and downstream teams to translate their access/filter requirements into routing rules.

Document pipeline architecture, data dictionaries, retention policies, and routing logic for auditability and seamless developer onboarding.

🛠️ Required Skills & Experience

Core Platform & SAP BODS

Hands-on experience with SAP Data Services (BODS 4.x) designing complex Batch Jobs, Real-time Services, Workflows, Data Flows, and custom Scripts/Functions.

Strong expertise in extracting data from SAP environments (SAP S/4HANA, ECC, BW, ODP extractors, SLT, IDocs, RFC/BAPIs) as well as non-SAP flat files, REST APIs, and relational databases.

Deep proficiency in SQL and relational database internals (e.g., SAP HANA, SQL Server, Oracle): query optimization, indexing, table partitioning, push-down SQL execution, and incremental CDC logic.

Solid experience with BODS Central Repository management, object check-in/out workflows, version control, and multi-user development standards.

Metadata-Driven Engineering (Critical)

Demonstrable background designing or maintaining metadata/config-driven ETL frameworks — e.g., control/registry tables driving dynamic SQL generation, parameterized BODS global variables, or generic reusable data flow templates.

Strong understanding of keeping generated ETL processes and target tables in sync with a source-of-truth ruleset, including schema versioning and change auditability.

Experience implementing automated audit logging, error handling, row-count reconciliations, and execution metrics captured directly via BODS scripts and database tables.

Platform Engineering / DataOps

ETL Orchestration & Scheduling: Experience managing automated job schedules and dependency management via BODS Management Console, Azure Data Factory, Control-M, Airflow, or Autosys.

CI/CD & Version Control: Experience applying software engineering discipline to BODS (e.g., Git integration/documentation, automated repository migrations, deployment scripts).

Scripting & Automation: Strong scripting skills using Python, PowerShell, or Bash to automate file transfers, API interactions, and operational tasks around the BODS platform.

Monitoring & Observability: Hands-on experience setting up alerting, performance bottleneck troubleshooting (memory/buffer tuning in BODS), and data validation frameworks.

Governance & Collaboration

Working knowledge of enterprise data governance: access control models, data lineage, retention/compliance-driven deletion, and data masking/obfuscation techniques for sensitive data (PII).

Experience serving multiple downstream consumers with varying analytical and operational requirements by delivering filtered and governed interfaces.

Clear written communication habits; ability to articulate complex ETL architectures and governance rules to both technical and non-technical stakeholders.

Nice to Have

Experience with SAP Information Steward (data quality profiling, rule building, metadata management).

Hands-on experience integrating SAP BODS with cloud data platforms (Azure Data Lake Storage Gen2, Snowflake, Databricks, or AWS S3).

Exposure to SAP Landscape Transformation (SLT) or SAP Data Intelligence / Datasphere.

Experience in large-scale multi-tenant enterprise environments supporting cross-functional business domains (Supply Chain, Finance, Commercial).

What Success Looks Like in the First 6 Months

Config-Driven Onboarding: Standard source table onboarding (source → landing/staging) is reduced to a configuration-driven process using reusable BODS templates, drastically reducing manual job creation.

Automated Retention & Purging: Automated partition/date-based archiving and retention mechanisms are operational across s