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Mid-level, common data-engineer skillset and metro presence yield moderate competition.
Data engineering skills transferable across industries but integration and governance needs increase domain specificity.
Moderate strictness due to explicit 5+ years, mandatory SQL/XML, ETL and partner-integration experience.
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Define and maintain canonical data models, contracts, and schema standards to ensure consistency in partner integrations.
Design and implement data validation frameworks and observability tooling including logging, lineage tracking, alerting, and failure dashboards.
Serve as internal escalation point for integration issues and collaborate with cross-functional teams to support scalable ingestion and standardized integration frameworks.
Degree in a related field.
5+ years of data engineering experience operating independently in complex, cross-functional environments.
Proficiency in Python (preferred) and SQL (required); strong knowledge of JSON, CSV, and XML (XML strongly preferred).
Experience with integration design validation, ETL/ELT processes, data transformations, and observability practices.
Experienced working with external data producers (partners, vendors, customers) and validating their data mappings and schemas.
Strong analytical skills with the ability to communicate complex technical concepts to both technical and non-technical stakeholders.
Familiarity with agile environments, version control using GitHub, AWS, Snowflake, dbt, and data observability tools such as Airflow or Great Expectations is a plus.