Senior Data Analytics Engineer, Commercial Team

Novellia•New York, NY

About The Position

We are looking for a Data Analytics Engineer to build and lead our commercial data science function from the ground up. This is a unique opportunity to define how a healthcare data company delivers world-class data and analytics solutions to biopharma clients while simultaneously building the internal data solutions that power our operations. This role is first and foremost, client-facing. You will be the analytics voice to our clients, joining weekly meetings to present patient engagement metrics, growth trends, and study insights. You'll field questions about our solutions, respond to ad-hoc analytical requests, and build trusted relationships with biopharma stakeholders who rely on your expertise to make critical decisions. Beyond client delivery and support of the commercial team, the ideal candidate will bring the aptitude and eagerness to contribute to broader data engineering capabilities. If you're curious about what that technical layer looks like, we encourage you to read the Analytics Engineering Lead job description — we're building both roles to work closely together. This role will partner closely with the Analytics Engineering Lead, who owns the core data infrastructure. You will focus on insight delivery and client-facing analytics built on top of that foundation. You will report directly to the Chief Customer Officer and be accountable for all client-facing metrics, analytics deliverables, and the strategic evolution of our commercial data science capabilities. We're a growing team tackling complex problems in healthcare data. We move quickly, prioritize impact, and take pride in doing things right. The truth is: this is a demanding job. It requires focus, urgency, and deep client accountability. If you're looking for a predictable environment with clear boundaries, this isn't the right fit. But if you want to build something from scratch, take real ownership of client relationships worth millions, and see your analytics directly shape biopharma research decisions—you'll find the challenge deeply rewarding.

Requirements

5-8 years of experience in data science or analytics, with at least 2 years in a client-facing or consulting capacity
Strong working knowledge of cloud data warehouse platforms (e.g. Snowflake, BigQuery, Redshift), with deep proficiency in SQL
Proficiency in Python for data analysis, statistical modeling, and automation
Strong command of data visualization tools (e.g. Metabase, Looker) and ability to design and implement dashboards for non-technical audiences
Experience building analytics solutions from scratch—data models, pipelines, reporting frameworks, and dashboards
Proven track record of translating ambiguous business questions into concrete analytical approaches and deliverables
Exceptional ability to present complex analytical insights to diverse audiences, from software engineers to C-suite biopharma executives
Demonstrated success building trusted relationships with external stakeholders through consistent, high-quality delivery
Comfortable managing competing priorities from multiple clients while maintaining quality and responsiveness
Strong individual contributor who can design and build solutions independently
Familiarity with advanced analytics techniques including statistical modeling, machine learning fundamentals, and predictive analytics
Ability to evaluate data quality, identify issues, and implement monitoring systems
Self-directed and resourceful—you identify problems and solve them without waiting for direction
Comfortable with ambiguity and rapid change in a growing company environment
Proven ability to manage competing priorities in fast-paced environments
Experience using modern and emerging AI tools (ChatGPT, Claude, Copilot, Cursor, etc.) to enhance productivity and accelerate delivery

Nice To Haves

Experience working with healthcare data (EHR, claims, registry data) is a strong plus but not required
Familiarity with tools like dbt is a plus, or a willingness to learn.

Responsibilities

Join weekly client meetings with biopharma partners to present patient engagement metrics, enrollment trends, data quality insights, and study progress
Serve as the primary analytics point of contact for client and commercial team inquiries, responding to questions about our data, methodologies, and analytical capabilities
Build meaningful relationships with biopharma stakeholders including medical affairs, clinical operations, and real-world evidence teams
Translate complex analytical findings into clear, actionable insights tailored to biopharma decision-makers. Success in this role requires strong collaboration with Engineering and to ensure solutions are scalable and reproducible, not one-off analyses
Proactively identify opportunities to deliver additional value through analytics and communicate these to clients
Partner with the internal Engineering team to contribute to and, where needed, support upstream data pipeline and dbt model development. Familiarity with tools like dbt is a plus, or a willingness to learn.
Partner closely with the solutions management team to translate client research and analytical needs into clear product requirements and technical specifications
Work with the Engineering team and the Analytics Engineering Lead to ensure commercial data deliverables are scalable, reproducible, and aligned with the broader data platform — not one-off analyses.
Drive data quality initiatives to ensure accuracy and reliability across all client-facing analytics
Define and develop best practices for research-grade analytics delivery in the real-world evidence space
Contribute to business development efforts by articulating our analytical capabilities to prospective clients
Identify patterns across client engagements that inform product development and company strategy