Head of Engineering - ETL / Data Lake / Salesforce ecosystem

Our client is building a portfolio of enterprise software for the Salesforce ecosystem. The group acquires best-in-class Salesforce-ecosystem SaaS companies and gives their founders the firepower to scale — world-class go-to-market, product engineering, security, and operational muscle. The Role As Head of Engineering, you will lead the development of the unified engineering platform and shared infrastructure underpinning critical, customer-facing capabilities across the group's Salesforce-ecosystem products — including Salesforce data protection and lifecycle management (backup, archive, recovery, replication, and consumption of application data), enterprise AI enablement, workflow automation, large-scale document management, and service and sales operations optimisation. The Impact You Will Have Execution Ownership The Product Suite The platform is designed to help customers protect their Salesforce data, manage it through its full lifecycle, and turn that same dataset into value faster. The engineering organisation you will lead owns and evolves the following product suite: Salesforce & other Data Replication Backup and Restore . Data Archive Time Machine . Data Lake /Data Lakehouse API Integration to Snowflake, Databricks and others. Observability. What We Look For 15+ years of software engineering experience with a strong track record of technical leadership and impact. 7-10 years of engineering management experience, including experience managing other managers. Proven experience scaling engineering teams from around 10 to 30+ engineers. Experience leading platform teams and backend engineers within a SaaS environment, ideally within or adjacent to the Salesforce ecosystem. Experience designing and building scalable, distributed systems. Familiarity with building services on top of containerisation technologies. Expertise across distributed systems, major cloud platforms (AWS, Azure, GCP), and modern web application architectures. Salesforce core objects, metadata, and multi-org architecture; AppExchange-listed applications. AWS (including S3-based data lakes and AWS Marketplace-distributed solutions). Microsoft Azure (including Azure-based deployments and Azure Marketplace). Heroku for platform-hosted application components. Snowflake and Databricks for AI-ready data activation and lakehouse architectures. Google BigQuery and Amazon Athena for query-in-place analytics. Amazon Redshift and Azure Synapse as enterprise data warehouse destinations. Tableau and other BI tools consuming continuously replicated SaaS application data. Please note: ONLY USA Nationals or Green Card holders will be considered. #J-18808-Ljbffr Yaz Associates GmbH