Data Engineer

 About SonderMind 

At SonderMind, we know that therapy works. SonderMind provides accessible, personalized mental healthcare that produces high-quality outcomes for patients. SonderMind's individualized approach to care starts with using innovative technology to help people not just find a therapist, but find the right, in-network therapist for them, should they choose to use their insurance. From there, SonderMind's clinicians are committed to delivering best-in-class care to all patients by focusing on high-quality clinical outcomes. To enable our clinicians to thrive, SonderMind defines care expectations while providing tools such as clinical note-taking, secure telehealth capabilities, outcome measurement, messaging, and direct booking.

To follow the latest SonderMind news, get to know our clients, and learn about what it’s like to work at SonderMind, you can follow us on Instagram, Linkedin, and Twitter.

About the Role

In this role, you will be responsible for designing, building, and managing the information infrastructure systems used to collect, store, process, and distribute data. You will also be tasked with transforming data into a format that can be easily analyzed. You will work closely with data engineers on data architectures and with data scientists and business analysts to ensure they have the data necessary to complete their analyses and to provide production service support on the data and their transformation whenever needed.

Essential Functions

• Strategically design, construct, install, test, and maintain highly scalable data management systems that include distributed databases, data warehouses, and cloud storage, etc.
• Develop and maintain databases, data processing procedures, and pipelines
• Integrate new data management technologies and software engineering tools into existing structures
• Develop and implement processes for data mining, data modeling, and dataproduction
• Translate complex functional and technical requirements into detailed architecture, design, and high-performing software and applications
• Create custom software components and analytics applications in Python and SQL
• Troubleshoot data-related issues and perform root cause analysis to resolve them
• Manage overall pipeline orchestration
• Optimize data warehouse performance

What does success look like?

Success in this role will be measured by the seamless and efficient operations of our data infrastructure. This includes maintaining high availability, ensuring optimal performance, and implementing robust security measures. Achieving success will also involve proactive monitoring, timely issue resolution, and continuous improvements to our systems. The ideal outcome is a stable, scalable, and secure data environment that supports the organization's goals and enables data-driven decision-making. This includes minimal downtime, accurate and timely data delivery and the successful implementation of new technologies and tools. The individual will have demonstrated their ability to collaborate effectively to define solutions with both technical and non-technical team members across data science, engineering, product and our core business functions. They will have made significant contributions to improving our data systems, whether through optimizing existing processes or developing innovative new solutions. Ultimately, their work will enable more informed and effective decision-making across the organization.

Who You Are

• Bachelor’s degree in Computer Science, Engineering, or a related field
• Minimum three years experience as a Data Engineer or in a similar role
• Experience with data science and analytics engineering is a plus
• Experience with AI/ML in GenAI or data software - including vector databases - is a plus
• Proficient with scripting and programming languages (Python, Java, Scala, etc.)
• In-depth knowledge of SQL and other database related technologies such as NoSQL
• Experience with Snowflake, DBT, BigQuery, Fivetran, Segment, etc.
• Experience with AWS cloud services (S3, RDS, Redshift, etc.)
• Experience with data pipeline and workflow management tools such as Airflow
• Strong negotiation and interpersonal skills: written, verbal, analytical
• Motivated and influential – proactive with the ability to adhere to deadlines; work to “get the job done” in a fast-paced environment
• Self-starter with the ability to multi-task

Preferred experience 

• Master’s degree in Computer Science, Engineering, or a related field
• Knowledge of Docker and Kubernetes to manage data applications in a scalable and efficient way
• Proficiency with ETL (Extract, Transform, Load) tools like Apache NiFi, Talend, or Informatica is a big advantage