DAY TO DAY:

We are looking for an exceptional Staff Data Engineer to build the high performance foundation that powers our client’s internal business analytics. In this pivotal role, you will partner with a newly hired Staff Analytics Engineer to design and build the end-to end delivery of our data ecosystem, ensuring that our leadership team has the timely, actionable insights they need to make informed decisions. You will be responsible for building “the plumbing” of this data ecosystem, including the ingestion of data from diverse sources into our Azure data lake, transformation of data in Databricks, and delivery of gold layer data to visualization tools. You will enable the seamless flow of financial and operational data from source systems to decision-makers, eliminating the technical bottlenecks that delay critical business insights. This is a demanding role in a results-oriented environment with high expectations for agency, speed, and ownership. You will architect a system that can take raw data and form it into easily consumable visualizations that the leaders of our client will use. 

This team is a newly created team and operates as a stand alone function not part of any other function/team (not a part of a traditional IT team). This group will report in through the CEO of our client, and is a highly impactful team focused on the delivery of end to end consumables. 

• Infrastructure Architecture: Design and implement the core architecture for the company’s data ecosystem that will be used for business analytics. This includes the end to-end architecture from the data in source systems to delivery in visualizations.

• High-Scale Ingestion: Build robust Azure Data Factory pipelines to pull data from disparate "Source A" systems (Salesforce, ServiceNow, D365) to "Sink B" (Azure datalake).

• Standards & Governance: Set the technical standards for the Business Operations engineering team. You will define how the team handles CI/CD, version control, and data quality testing at the ingestion level.

• System Reliability: Ensure the raw and bronze data layers are available and up-to-date, minimizing downtime.