Senior Data Engineer - Engineering

Purpose of Job

• This role builds front-end and back-end infrastructure for the firm’s in-house enterprise data platform as well as explores, enables, and documents new technologies. This position also works independently to support client teams with different data needs, including deployment for new clients, support for existing clients, development of backend datasets for dashboards, and support for the front-end.

We'll trust you to:

• Ensures reliability and stability of the enterprise data platform and tools used by internal teams and clients.
• Demonstrates knowledge of the data platform components (warehouse, monitoring, language, etc.) and data engineering (ETL design, schema design, etc.) and performs unit testing.
• Builds and enhances data integration, management, and analytics tools and pipelines.
• Documents code, deployments, best practices, and improvements.
• Designs and builds ETL/ELT data systems, including cloud-based APIs, automated data transfer systems, and code management solutions.
• Acts as a regular point of contact for data engineering projects and collaborates with the internal consulting team and the data platform and technology teams to complete projects for clients successfully.
• Participates in and contributes ideas to brainstorm, design, and implement more complex processes and analyses to solve challenging and abstract problems.
• Develops and implements best practice guidance and supporting materials for data management and advanced analytics pipelines.
• Creates high-quality presentations, deliverables, and code that meet objectives, with project manager guidance.
• Performs other job-related duties as assigned.

You'll need to have:

• At least 5 years’ experience in data engineering using Python, including the use of pandas or PySpark.
• Experience working with Databricks extensively (including installing packages, understanding and setting cluster configurations, managing jobs, user management, handling permissions, managing Unity Catalogues, Databricks APIs and AI tools, and handling configuration issues) and relational database technologies, such as PostgreSQL, Oracle, MySQL, Redshift, Snowflake.
• Software development fundamentals, including Agile development, version control systems such as Git or DevOps, code reviews, testing, and documentation.
• Experience working with AI tools.
• Experience configuring AzureAD/SAML/Okta/Oauth and administering AWS or Azure security best practices, preferred.
• Experience with WYSIWYG ETL tools (Azure Data Factory, Informatica, SnapLogic, Boomi), preferred.
• Container orchestration systems experience using Docker, Kubernetes, AWS ECS, preferred.
• At least 6 months experience in web application development using Flask, Django, JavaScript, Ajax, or CSS/HTML is preferred.
• Candidates holding a recognized data engineering certification are preferred (e.g., Databricks Data Engineer, Google Cloud Professional Data Engineer, Azure Data/Fabric Data Engineer, AWS Certified Data Engineer).
• Proficiency using Microsoft Office products, including Excel, PowerPoint, and Word.
• Life Sciences industry experience, preferred.




• Desired Soft Skills:


• Excellent communication skills. Ability to communicate with business stakeholders as well as technical development teams
• Strong problem-solving skills, excellent critical and creative thinking skills and ability to brainstorm ideas
• Ability to work in a team and collaborate across teams effectively
• Ability to adapt quickly to changing technologies
• Ability to assess and identify bottlenecks and potential failures