Description:
If you are looking to join a small, driven, and growing team where you can leverage your data engineering experience to build highly impactful digital solutions that impacts the lives of our patients and healthcare providers, then this is the position for you. You will be the senior data engineer within the team. You will be responsible for working within project teams and ensuring the solution architecture creates delightful experience for internal and external stakeholders.
Key Duties And Responsibilities
- Design, build, and maintain cloud based ETL (extract, transform, load) processes, data pipelines, data warehouses, and data lakes to extract medical data (databases - patient data, images, or videos)
- Lead architectural design of enterprise-grade data engineering platforms that meet compliance, regulatory, performance/scalability, and operational requirements.
- Develop standards, procedures, techniques, and tools to accelerate data engineering projects.
- Build dashboards to convey clinical insight to multi-disciplinary teams (clinical, marketing, engineering).
- Effectively communicate with and influence key stakeholders across the enterprise, at all levels of the organization
- Be part of a high-performing team developing and implementing AI algorithms to help address solve complex problems and create unique solutions in healthcare.
- Partner closely with data scientists, and machine learning engineers, and other technical teams to optimize our delivery process for production grade ML/AI systems and dashboards.
- Build awareness, increase knowledge, and drive adoption of data engineering technologies and architecture patterns, sharing customer and engineering benefits to gain buy-in (working closely with leaders, other SMEs, and engineers)
- Maintain knowledge of advances of data engineering systems, techniques, open-source, and cloud offerings
- Strong engineering fundamentals in software design, version control, continuous integration and delivery, testing, containers and orchestration, monitoring and logging for enterprise-grade machine learning platforms in real world environments.
- Carryout architecture reviews and enabling teams fix issues that arise from the reviews.
- Ability to effectively communicate, influence and drive consensus between the business, technology teams, and executive leadership in an organization with multiple lines of business.
- Responsible for working in an Agile environment and evaluating pre-project ideas in terms of technical complexity and solution architecture. Participates in high-level estimation.
About You
- Bachelor’s degree in computer science, Data Analytics, Software or Computer Engineering, Computational Statistics, Mathematics, or a closely related discipline. Master’s or above is highly preferred.
- Experience working in a regulated industry, or the medical device space is highly preferred.
- 5+ years of experience in a data engineer role covering designing, building and maintaining ETL (extract, transform, load) processes, data pipelines, data warehouses, and data lakes.
- Demonstrable experience in using cloud-based platform including AWS, Google cloud and Azure. Experience in building pipelines and data warehouses and lakes in AWS and Databricks is preferred. Use of Terraform to build cloud-based infrastructure is also desirable.
- Demonstrable expertise using scripting and database languages such as: Python, SQL, and Scala
- Experience building pipelines (ingestion and data lakes) for databases, images and videos
- Experience in building dashboards to convey insight to multi-disciplinary teams (clinical, marketing, engineering). Tools may include Power BI, streamlit, Tableau or other BI tools.
- Experience working with RESTful API and general event-driven architectures.
- Understanding of DevOps and MLOps tool chains and processes, including ML model deployment. Proven expertise in data curation and data architecting to support machine learning projects. Has solutioned complex data processing and storage challenges through scalable, fault-tolerant architecture
- Familiarity with tools and technologies in the data engineering ecosystem for Data Storage (S3, Hive, Cassandra, Lakehouse, etc.), Data Pipelines (ffmpeg, Kafka, Airflow, Prefect, etc.) and datalakes (Snowpipe, AWS, Databricks etc.)