Forward Deployed Data Engineer

SAP•New York, NY

3d•$90,000 - $198,500•Hybrid

About The Position

As a Forward Deployed Data Engineer, you will design and implement modern enterprise data platforms that enable AI applications, analytics, and business processes. Working directly with customers, you will build scalable data pipelines, data models, semantic layers, and integrations across SAP and non-SAP systems. This role is intended for engineers with 3–7 years of experience who enjoy solving complex data challenges in customer-facing environments.

Requirements

Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or a related discipline.
3–7 years of professional experience in data engineering or platform engineering.
Strong SQL and Python programming skills.
Hands-on experience with Apache Spark, Databricks, Kafka, Airflow, or similar modern data platforms.
Experience designing enterprise data models, data lakes, lakehouses, and data warehouses.
Experience with PostgreSQL, SAP HANA, Snowflake, BigQuery, or equivalent databases.
Knowledge of data governance, lineage, metadata management, and data quality practices.
Experience with Docker, Kubernetes, Git, CI/CD, and cloud platforms (AWS, Azure, or GCP).
Exposure to SAP Datasphere, SAP HANA Cloud, SAP Integration Suite, SAP BTP, or SAP AI Core is highly desirable.
Strong analytical thinking, customer engagement, and communication skills.

Nice To Haves

Experience supporting AI/ML workloads through feature stores, vector databases, or embedding pipelines.
Knowledge of Knowledge Graphs, GraphRAG, or semantic technologies.
Experience with dbt, Iceberg, Delta Lake, or Apache Flink.
Open-source contributions or experience with AI-assisted engineering tools such as GitHub Copilot or Cursor.

Responsibilities

Design and implement scalable batch and real-time data pipelines.
Build data ingestion frameworks integrating SAP and non-SAP enterprise systems.
Develop logical and physical data models, semantic layers, and business schemas for AI applications.
Build ELT/ETL pipelines using SQL, Python, Spark, and modern data engineering frameworks.
Implement data quality, lineage, governance, metadata management, and validation processes.
Develop APIs and data services that expose enterprise data to AI agents and applications.
Optimize data storage, query performance, and distributed processing workloads.
Deploy and operate data platforms on SAP BTP, Kubernetes, hyperscalers, and cloud-native environments.
Collaborate with AI engineers, solution architects, and customer stakeholders to translate business requirements into robust data solutions.
Create reusable accelerators, reference data models, and engineering best practices.