Senior Data Engineer

OneMagnify is an AI native, platform-enabled B2B digital agency operating at the intersection of data, technology, and creativity. As a Data Engineer, you'll build and maintain the data pipelines and integrations that power enterprise analytics and client-facing solutions, ensuring data reliability and quality for large B2B clients.

Responsibilities:

• Develop integrations between data sources and target systems including ERPs, CRMs, and data warehouses using Databricks and AWS-native services (Glue, Step Functions, Lambda)
• Configure, customize, and deploy data engineering applications that support multiple data domains reliably and at scale
• Leverage the Databricks Lakehouse platform — Delta Lake, Unity Catalog, and Spark-based processing — to optimize pipeline performance and maintainability
• Develop and enforce data cleansing and standardization guidelines that keep data consistent and trustworthy across systems
• Use strong SQL skills to validate, troubleshoot, and resolve data issues before they surface downstream
• Partner with data architects to set quality standards that the broader team can operate against
• Build integrations using APIs and modern pipeline approaches to connect systems that weren't designed to work together
• Align pipeline design with enterprise data flows in close collaboration with data scientists and analysts
• Ensure integrations are built for durability, not just initial delivery
• Work directly with business users and data stewards to diagnose and resolve data issues within the platform
• Translate technical pipeline behavior into clear explanations for non-engineering stakeholders
• Contribute to documentation and processes that make the data platform easier to use and maintain over time

Requirements:

• Bachelor's degree in Computer Science, Information Systems, or a related field — or equivalent professional experience
• 5+ years of hands-on experience in data engineering development or implementation
• Strong SQL skills across data analysis, validation, and troubleshooting
• Hands-on experience with Databricks (Delta Lake, Unity Catalog, Spark) and AWS data services (Glue, Redshift, S3, Lambda, or Step Functions)
• Familiarity with APIs and integration methods for connecting systems across an enterprise
• Experience with Databricks MLflow or Feature Store supporting AI/ML pipeline workflows
• Familiarity with marketing data ecosystems: CRM platforms, CDP architectures, or Martech/Adtech data flows
• Exposure to data observability or governance tooling (lineage tracking, data cataloging, pipeline monitoring)
• Experience in a digital agency, marketing services, or consulting environment with multiple concurrent client data environments
• Working knowledge of streaming data pipelines or event-driven architectures (e.g., Kafka, Kinesis)