Summary
✨ AI‑Generated
A lead data engineering role responsible for designing and implementing enterprise data platforms, scalable pipelines, cloud architectures, and engineering standards.
Highlights
Lead role building enterprise-scale data platforms with modern cloud technologies and architectural ownership.
Description
About the Role:
Build the enterprise’s AWS Databricks data lake, including the lakehouse foundation, ingestion patterns, pipeline frameworks, and target data model.
This role owns the technical buildout and design leadership for migrating key pipelines from Microsoft Fabric into AWS Databricks and implementing them into a new, scalable data model.
Key Responsibilities:
Build the AWS Databricks data lake and lakehouse foundation, including workspace, storage, compute, catalog, security, and environment patterns.
Architect and build ingestion into Databricks from SAP S/4HANA, Salesforce, IoT, complaints, Microsoft Fabric, and other enterprise systems.
Design the target enterprise data model and implement migrated Microsoft Fabric pipelines into that model in AWS Databricks.
Implement Git, CI/CD, and engineering best practices.
Define platform standards for optimization, reliability, security, cost management, and handoff to DataOps for ongoing operations.
Mentor company’s engineering resources.
Required Qualifications:
10+ years of experienceExperience on AWS Databricks data platform architecture.
Lakehouse, data lake, medallion-style patterns, and Databricks integration experience.
Enterprise integration and pipeline orchestration for S/4HANA, Salesforce, IoT, and complaints data.
Data modeling and platform engineering.
CI/CD, infrastructure-as-code, and DevOps practices.
Technical leadership and mentoring.
Preferred Locations:
USA & Canada
