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Tier-1 brand, metro role, mid-level data engineer title, and broad skill requirements increase applicant competition.
Requires Snowflake, Airflow, Neo4j and HR analytics familiarity, making background moderately transferable.
Explicit 6+ years and mandatory Snowflake, Airflow, Neo4j, and CI/CD requirements create strict filtering.
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Engineer and optimize end-to-end data pipelines using Apache Airflow and Python for scalable, high-performance data delivery within Salesforce Employee Success functions.
Design and implement robust data models and data foundations in Snowflake and Salesforce Data360 to serve as enterprise sources of truth, including management of Neo4j graph databases.
Own CI/CD pipeline management including automated testing, deployment, and data integration with various platforms (Snowflake, AWS Lambda, S3, APIs) ensuring data integrity and operational efficiency.
6+ years of relevant work experience in information systems and data engineering.
Proficiency in advanced SQL, Python, Bash, Apache Airflow, Snowflake, and CI/CD pipeline tools with Git version control.
Experience in designing and automating complex ETL/ELT data pipelines and data modeling (Star/Snowflake schemas).
Degree or equivalent relevant experience is required; notice period not explicitly mentioned.
Experienced in People Analytics or HR technology domains with understanding of related data structures (e.g., Workday) to align technical solutions to business needs.
Skilled in building scalable, repeatable data frameworks with strong problem-solving skills and agility in adapting technical strategies under shifting business priorities.
Proven ability to collaborate cross-functionally with business and engineering teams to translate requirements and deliver high-quality proofs of concept and documentation.