Technical Data Engineer - GTM Services Marketing

We help the world run better

WHAT YOU LL BUILD:

For our Go-To-Market Services for Marketing team as part of the Customer Partner unit within the global Corporate Processes Information Technology (CPIT) Organization we are looking for a Technical Data Engineer with the mindset for highest level of service for our internal customers and support to take SAP s Marketing Technology Stack to the next level as part of our transformation journey to achieve SAP s goals.

In your role as Technical Data Engineer, you will be responsible for:

• Design, build, and maintain scalable batch and streaming data pipelines and ELT workflows that power analytics, ML, and operational use cases.
• Own the data platform end-to-end across ingestion, transformation, modeling, orchestration, quality, observability, and cost/performance optimization in the cloud (AWS/GCP/Azure).
• Implement robust data models (warehouse and lakehouse), versioned transformations (e.g., dbt/Spark), and enforce governance, security, and compliance standards.
• Partner with product, analytics, ML, and engineering teams to translate requirements into reliable data contracts, SLAs, and self-serve datasets.
• Troubleshoot and resolve production issues, automate testing and deployments (CI/CD, Infrastructure as Code (IaC)), and document systems to enable team-wide operational excellence.

WHAT YOU BRING:

• 7 - 10 years building and operating data pipelines at scale with Python and SQL, including Spark or similar distributed frameworks.
• Expertise with modern ELT tooling and orchestration (dbt, Airflow/Dagster/Prefect) and version-controlled, testable transformations.
• Strong data modeling in warehouses and lakehouses (Kimball, Data Vault, medallion), schema evolution, and performance tuning.
• Cloud data platforms (AWS/GCP/Azure): storage (S3/GCS/ADLS), computing (EMR / Dataproc / Databricks / BigQuery / Snowflake) , and networking fundamentals.
• Streaming and event-driven architectures (Kafka/Kinesis/Pub/Sub), exactly-once semantics, backfills, and idempotency.
• Data quality, observability, and reliability practices (Great Expectations/Soda/Monte Carlo), SLAs, alerting, and incident response.
• CI/CD and infrastructure-as-code (GitHub Actions/GitLab CI, Terraform/CloudFormation), containerization (Docker, Kubernetes).
• Security and governance: IAM, secrets management, encryption, PII handling, GDPR/CCPA awareness, and access patterns (RBAC/ABAC).
• Experience enabling downstream consumers (BI/ML) via data contracts, documentation, and self-serve datasets/semantic layers (Looker/MetricLayer).