Data Engineer

We are seeking a Data Engineer to join our dynamic team. The ideal candidate is an enthusiastic problem-solver who excels at building scalable data systems and has hands-on experience with Databricks, Looker, AWS, MongoDB, PostgreSQL, and Node.js. You will work alongside sales, customer success and engineering to design, implement, and maintain a robust data infrastructure that powers our analytics and platform offerings.

Key Responsibilities

1. Data Pipeline & Integration
• Design, build, and maintain end-to-end data pipelines using Databricks (Spark) for efficient data ingestion, transformation, and processing.
• Integrate data from various structured and unstructured sources, including medical imaging systems, EMRs, and external APIs.
2. Analytics & Visualization
• Collaborate with the analytics team to create, optimize, and maintain dashboards in Looker.
• Implement best practices in data modeling and visualization to deliver actionable insights.
3. Cloud Infrastructure
• Deploy and manage cloud-based solutions on AWS (e.g., S3, EMR, Lambda, EC2) to ensure scalability, availability, and cost-efficiency.
• Develop and maintain CI/CD pipelines for data-related services and applications.
4. Database Management
• Oversee MongoDB and PostgreSQL databases, including schema design, indexing, and performance tuning.
• Ensure data integrity, availability, and optimized querying for both transactional and analytical workloads.
5. Backend Development & APIs
• Utilize Node.js to build and maintain RESTful APIs and microservices for data ingestion, transformation, and application integration.
• Implement robust error handling, logging, and monitoring frameworks to ensure reliability and transparency.
6. Security & Compliance
• Adhere to healthcare compliance requirements (e.g., HIPAA) and best practices for data privacy and security.
• Implement data governance frameworks to maintain data integrity and confidentiality.
7. Collaboration & Documentation
• Work cross-functionally with data scientists, product managers, and other engineering teams to gather requirements and define data workflows.
• Document data pipelines, system architecture, and processes for internal and external stakeholders.
8. Innovation & Continuous Improvement
• Evaluate new technologies and methodologies to enhance data processing performance and scalability.
• Provide recommendations on emerging trends in data engineering, analytics, and cloud infrastructure.

Requirements

• Education & Experience
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 3+ years of professional experience in data engineering or a similar role.
• Technical Skills
• Databricks (Spark): Proven expertise in building large-scale data pipelines.
• Looker: Experience in creating dashboards, data models, and self-service analytics solutions.
• AWS: Proficient with core services like S3, EMR, Lambda, IAM, EC2, etc.
• MongoDB & PostgreSQL: Demonstrated ability to design schemas, optimize queries, and manage high-volume databases.
• Node.js: Skilled in developing RESTful APIs, microservices, and backend applications.
• SQL & Scripting: Strong SQL skills, plus familiarity with Python, Scala, or Java for data-related tasks.
• Soft Skills
• Excellent communication and team collaboration abilities.
• Strong problem-solving aptitude and analytical thinking.
• Detail-oriented, with a focus on delivering reliable, high-quality solutions.
• Preferred
• Experience in healthcare or imaging (e.g., DICOM, HL7/FHIR).
• Familiarity with DevOps tools (Docker, Kubernetes, Terraform) and CI/CD pipelines.
• Knowledge of machine learning workflows and MLOps practices.

Benefits

• Health Care Plan (Medical, Dental & Vision)

• Retirement Plan (401k, IRA)
• Paid Time Off (Vacation, Sick & Public Holidays)
• Training & Development
• Work From Home