Data Solution Engineer

RA Capital Management • Boston, MA

7d • $120,000 - $180,000 • Hybrid

About The Position

As a Data Solutions Engineer, you will play a critical role in shaping how data powers investment decisions across breakthrough biomedical and climate technologies. You will work at the intersection of data engineering, analytics, AI-enabled workflows, and business strategy to build scalable solutions that transform complex datasets into actionable insights. In this role, you will partner directly with investment, operations, and business teams to identify opportunities where data and automation can drive smarter decisions and operational efficiency. You will design and deliver modern data solutions spanning analytics dashboards, cloud data platforms, lightweight pipelines, and AI-assisted workflows that support both strategic research and day-to-day business operations. You will join a collaborative, technically ambitious environment where curiosity, experimentation, and ownership are highly valued — and where the systems you build directly contribute to funding innovations that can improve and extend human lives.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Analytics, or a related technical field
• 2–5 years of experience building data, analytics, or business intelligence solutions in a professional environment
• Experience working with modern cloud-based data platforms and analytics tooling
• Experience working in fast-paced, highly collaborative environments with multiple stakeholders
• Ability to work in a hybrid environment in our Boston office, 2-3 days onsite per week
• Must be eligible to work in the US without sponsorship now or in the future, no transfers
• Strong proficiency in SQL and Python for analytics, data transformation, and pipeline development
• Experience with PySpark and distributed data processing frameworks
• Hands-on experience with Databricks, Snowflake, and AWS-based data infrastructure
• Proficiency with BI and analytics platforms such as Sigma Computing and Dash Enterprise
• Familiarity with orchestration, workflow automation, and lightweight ETL/ELT development
• Strong understanding of data modeling, data quality, and data governance best practices
• Curiosity and practical interest in applying AI, agents, and automation to data workflows and business problems
• Experience building MVPs, proofs-of-concept, or rapidly iterating on technical solutions
• Excellent English communication skills with the ability to work effectively across technical and non-technical teams
• Strong problem-solving mindset with a high degree of curiosity and ownership
• Ability to independently manage projects while balancing multiple priorities in a fast-paced environment
• Comfortable navigating ambiguity and identifying practical, scalable solutions
• Strong attention to detail in documentation, governance, and operational reliability

Nice To Haves

• Healthcare, biotechnology, life sciences, and/or financial services experience strongly preferred
• Familiarity with APIs, integrations, and modern software engineering practices is a plus
• Exposure to AI-assisted development tools and agentic workflows (e.g., Claude Code, Codex, MCPs, automation agents) strongly preferred

Responsibilities

• Design and deliver end-to-end data and analytics solutions, from initial concept and MVP through production deployment
• Build dashboards and analytical applications that provide actionable insights to investment and business stakeholders
• Develop and manage scalable data pipelines in partnership with the Data Engineering team, while independently owning lighter-weight workflows and integrations
• Support and administer modern cloud data platforms including Databricks, Snowflake, Sigma Computing, and Dash Enterprise
• Partner directly with business teams to identify data opportunities, gather requirements, scope projects, and translate business needs into technical implementations
• Prototype and evaluate new AI-enabled workflows, agents, and automation techniques to improve data accessibility and operational efficiency
• Apply and uphold data governance standards including data cataloging, metadata management, classification, and documentation best practices
• Monitor platform performance, manage user access, and help optimize the reliability and scalability of our data ecosystem
• Document technical processes, data models, and project outcomes to ensure maintainability and knowledge sharing across the organization
• Contribute to a collaborative, mentorship-driven engineering culture focused on continuous improvement and technical excellence

Benefits

• health insurance
• retirement contributions
• paid time off
• Employer-paid monthly premiums for health, dental, and vision coverage
• Wellness benefits and programs to support physical and mental well-being
• Resources and perks that enhance work-life balance and financial security

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level