NourishedRx is on a mission to eradicate poor diet and nutrition insecurity as top drivers of death, disease and disparities. Founded in 2019, NourishedRx is a digital health and nutrition company that helps people live healthier lives. Leveraging the healing and connective power of food, NourishedRx partners with healthcare organizations to nourish their most vulnerable members, build healthy relationships, and support health equity.
Poor diet is the top driver of death, disease and disparities in the US, and the relationship between diet and health is worsening at an alarming rate. 90% of US adults don’t get enough fruits and vegetables, 74% of Americans have overweight or obesity (more than triple the rate in 1960), 93% of US adults have poor cardiometabolic health, and 17 million US households are food insecure. These trends are heightened with racial and ethnic minority groups, contributing to health and economic disparities. Since 2019, NourishedRx has partnered with healthcare organizations to nourish their most vulnerable members, build healthy relationships, and support health equity.
Join us in revolutionizing healthcare through food.

SENIOR DATA ENGINEER

We are seeking an experienced and motivated Senior Data Engineer to join our dynamic data team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, managing our data warehouse infrastructure, and supporting analytics initiatives across the organization. You will work closely with data scientists, analysts, and other stakeholders to ensure data quality, integrity, and accessibility, enabling the organization to make data-driven decisions.

QUALIFICATIONS:

Education:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a related field, or equivalent practical experience.

Experience:

• 5+ years of experience in data engineering or a similar role.
• Proven experience building and managing data pipelines, data warehouses, and ETL processes.

Technical Skills:

• Strong proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL, Oracle) and data warehousing solutions (e.g., Snowflake, Redshift, BigQuery).
• Expertise in data pipeline tools and frameworks (e.g., AWS Glue, Google Dataflow, Apache Airflow, Apache NiFi, dbt).
• Hands-on experience with cloud platforms and their data services (e.g., AWS, Azure, Google Cloud Platform).
• Proficiency in programming languages such as Python, Java, or Scala for data manipulation and automation.
• Knowledge of data modeling, schema design, and data governance principles.
• Familiarity with distributed data processing frameworks like Apache Spark, Hadoop, or similar.
• Experience with BI tools (e.g., Tableau, Power BI, Looker)

Soft Skills:

• Strong problem-solving and analytical skills with a keen attention to detail.
• Excellent communication and collaboration skills, with the ability to work effectively with technical and non-technical stakeholders.
• Proactive mindset with the ability to work independently and handle multiple tasks in a fast-paced environment.

• Design and Develop Data Pipelines: Architect, develop, and maintain robust and scalable data pipelines for ingesting, processing, and transforming large volumes of data from multiple sources in real-time and batch modes.
• Data Warehouse Management: Manage, optimize, and maintain the data warehouse infrastructure, ensuring data integrity, security, and availability. Oversee the implementation of best practices for data storage, partitioning, indexing, and schema design.
• ETL Processes: Design and build efficient ETL (Extract, Transform, Load) processes to move data across various systems while ensuring high performance, reliability, and scalability.
• Data Integration: Integrate diverse data sources (structured, semi-structured, and unstructured data) into a unified data model that supports analytics and reporting needs.
• Data Quality and Governance: Establish and enforce data quality standards, governance policies, and best practices. Implement monitoring and alerting to ensure data accuracy, consistency, and completeness.
• Support Analytics and BI: Collaborate with data analysts, data scientists, and business intelligence teams to understand data requirements and provide data sets, models, and solutions that support their analytics needs.
• Performance Optimization: Optimize data pipelines and warehouse infrastructure for performance and cost-effectiveness, identifying and resolving bottlenecks and inefficiencies.
• Tool and Technology Evaluation: Evaluate and recommend new tools, technologies, and best practices to enhance the efficiency and scalability of our data infrastructure.
• Documentation and Training: Create and maintain detailed documentation for data pipelines, ETL processes, data models, and data warehousing solutions. Mentor and train junior team members and other stakeholders on data engineering practices.
• Collaboration and Stakeholder Management: Work closely with cross-functional teams, including engineering, analytics, and product teams, to define and prioritize data initiatives and ensure alignment with business goals.