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About
mission-driven organization supporting a vital medical professional community
is seeking a
Data Engineer
to join its Data Governance and Analytics team. This role focuses on building and optimizing scalable data pipelines, enabling advanced analytics and machine learning models, and supporting enterprise data governance standards. You'll work on high-impact initiatives that shape data strategy and architecture across the organization.
The ideal candidate is a hands-on engineer with expertise in
Databricks ,
Azure , and
CI/CD automation , combined with strong Python and SQL skills. You'll lead technical best practices, mentor peers, and drive innovation in data engineering and DevOps.
Minimum Qualifications:
Bachelor's degree in Computer Science, Engineering, or related field 7+ years building enterprise-level data solutions on cloud platforms Proven expertise in Databricks, Azure, Python, SQL, and Apache Spark Strong experience with CI/CD automation and DevOps practices Familiarity with data governance frameworks and security compliance Excellent collaboration and communication skills Nice To Have:
Experience in Non-Profit, Associations, or Mission Driven Organizations Responsibilities:
Design and optimize scalable data pipelines for analytics, AI/ML, and BI workloads Implement ETL/ELT solutions for multi-source data ingestion and transformation Drive DevOps adoption with CI/CD automation and Infrastructure as Code Manage and optimize Azure cloud data storage for cost and performance Ensure data quality, integrity, and compliance with governance standards Collaborate with architects, analysts, and business stakeholders to deliver data-driven solutions Mentor junior engineers and foster continuous improvement Evaluate emerging technologies to enhance scalability and innovation Desired Skills:
Databricks
- Advanced experience with Databricks Workflows for orchestrating production-grade pipelines Azure Cloud
- Expertise in Azure Data Factory, Data Lake, Synapse, Key Vault, and Monitor CI/CD & DevOps
- Automated deployments, Infrastructure as Code (Terraform), GitHub Actions, Azure DevOps Programming
- Strong Python and SQL for ETL/ELT, analytics, and ML workflows Apache Spark (PySpark)
- Distributed data processing and real-time analytics Data Governance
- Data quality, lineage, cataloging, and compliance frameworks ETL/ELT Design
- Structured streaming, batch ingestion, orchestration (Airflow, Kafka) RESTful APIs
- Integration with OAuth authentication, JSON parsing, and scalable pipelines Mentorship
- Ability to guide junior engineers and promote best practices
Languages
- English
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