About Lumen:

Lumen is a pioneering health-tech startup on a mission to revolutionize weight loss and well-being. Our innovative metabolic measurement device provides users with a comprehensive understanding of their metabolism, empowering them with personalized, data-driven insights to make informed lifestyle choices.

At Lumen, data is at the core of everything we do. We collect and analyze vast amounts of user data from our device and app to provide personalized recommendations, enhance our product, and drive advancements in metabolic health research. As we continue to scale, our data infrastructure is crucial to our success and our ability to empower our users.

About the Role:

As a Senior Data Engineer at Lumen, you’ll be more than just a coder - you’ll be the architect of our data ecosystem. We’re looking for someone who can design scalable, future-proof data pipelines and connect the dots between DevOps, backend engineers, data scientists, and analysts.

You’ll lead the design, build, and optimization of our data infrastructure, from real-time ingestion to supporting machine learning operations. Every choice you make will be data-driven and cost-conscious, ensuring efficiency and impact across the company.

Beyond engineering, you’ll be a strategic partner and problem-solver, sometimes diving into advanced analysis or data science tasks. Your work will directly shape how we deliver innovative solutions and support our growth at scale.

Responsibilities:

• Design and Build Data Pipelines: Architect, build, and maintain our end-to-end data pipeline infrastructure to ensure it is scalable, reliable, and efficient.
• Optimize Data Infrastructure: Manage and improve the performance and cost-effectiveness of our data systems, with a specific focus on optimizing pipelines and usage within our Snowflake data warehouse. This includes implementing FinOps best practices to monitor, analyze, and control our data-related cloud costs.
• Enable Machine Learning Operations (MLOps): Develop the foundational infrastructure to streamline the deployment, management, and monitoring of our machine learning models.
• Support Data Quality: Optimize ETL processes to handle large volumes of data while ensuring data quality and integrity across all our data sources.
• Collaborate and Support: Work closely with data analysts and data scientists to support complex analysis, build robust data models, and contribute to the development of data governance policies.

Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Experience: 5+ years of hands-on experience as a Data Engineer or in a similar role.
• Data Expertise: Strong understanding of data warehousing concepts, including a deep familiarity with Snowflake.

Technical Skills:

• Proficiency in Python and SQL.
• Hands-on experience with workflow orchestration tools like Airflow.
• Experience with real-time data streaming technologies like Kafka.
• Familiarity with container orchestration using Kubernetes (K8s) and dependency management with Poetry.
• Cloud Infrastructure: Proven experience with AWS cloud services (e.g., EC2, S3, RDS).