Design and implement specialized data structures that support the use of customer data graphs, which power agentic context and memory
Lead the development of robust ETL/ELT frameworks using Python and SQL, building highly decoupled, modular pipelines that can handle petabyte-scale data
Build customer identity graphs that serve data to applications and AI with sub-second performance
Act as a technical pillar for a specialized team of data and AI engineers, fostering technical excellence
In partnership with product managers and engineering leaders, align graph strategy and architecture with broader Data360 efforts
Establish and enforce rigorous technical standards for data quality and latency to ensure reliable, real-time insights
Lead high-impact efforts to automate the data delivery pipeline, ensuring seamless integration between internal databases, third-party APIs, and the AI orchestration layer.

Requirements

8+ years of experience as a Data Engineer or in a similar role
A related technical degree required
Proficiency in data engineering tools and languages, such as Python, SQL, and Spark
Strong understanding of database concepts, data modeling, and ETL processes with tools like Airflow, dbt, Informatica, etc.
Experience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud)
Familiarity with data warehousing, SQL, NoSQL databases, and data integration techniques
Experience with the Salesforce Ecosystem, specifically Data Cloud
Problem-solving skills to troubleshoot and resolve data-related issues
Excellent communication skills and ability to collaborate in a cross-functional environment.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLSparkETLELTdata modelingdata warehousingNoSQLdata integrationdata quality

Soft Skills

problem-solvingcommunicationcollaborationleadershiptechnical excellence

Certifications

related technical degree