Director of Engineering (Data & Analytics)

EVERSANA•New York, NY

About The Position

Position Overview Reporting to the VP of Product & Engineering, the Director of Engineering will lead a high-performing engineering team to design, develop, and optimize scalable data platforms and products. This role will partner closely with the VP to define and execute short- and long-term technology strategies and roadmaps that enable innovation and growth. The ideal candidate combines exceptional leadership, technical expertise, and strategic vision with strong communication and project management skills. This individual will champion modern technologies and practices to maximize productivity and accelerate Eversana’s AI/ML-driven initiatives.

Requirements

Bachelor’s degree in engineering, computer science, or related field
5+ years in a senior engineering leadership role (Director or equivalent).
10+ years building large-scale, distributed systems capable of ingesting and analyzing billions of events daily.
5+ years developing cloud-based solutions (SaaS, PaaS, IaaS, AIaaS).
Proven experience implementing AI/ML solutions and statistical modeling.
Strong background in presales/solutions engineering for enterprise customers.
Demonstrated success in building and scaling high-performing technical teams.
2-3 years in Government Pricing calculations and submissions
Excellent communication and stakeholder management skills.
Ability to simplify complex technical concepts for diverse audiences.
High emotional intelligence and proven success leading remote teams.
Architecture Leadership Backend: Python (FastAPI, Flask, Django, AsyncIO), Node.js/TypeScript (React, Next.js).
Data: PySpark, Pandas, NumPy; ETL (Airflow, dbt); distributed systems (Spark).
Streaming: Kafka, RabbitMQ.
Datastores: Snowflake, PostgreSQL, MySQL; Redis, MongoDB, DynamoDB; Trino/Presto; Apache Iceberg.
Cloud & Infrastructure Champion cloud-native designs on AWS (EC2, Lambda, ECS, S3/EFS, ELB/CloudFront, Cognito, Shield, Secrets Manager/Vault, CloudWatch/X-Ray, RDS, OpenSearch, ElastiCache, SQS/SES, Bedrock).
Experience with Azure and GCP ecosystems.
DevOps & Observability Containerization/orchestration: Docker, Kubernetes, Helm.
CI/CD: ArgoCD, Jenkins, GitHub Actions.
Observability: Prometheus, Grafana, Loki, Jaeger, OpenTelemetry.
Distributed system design and trade-offs.
Big data technologies (Spark), scientific computing.
AI/ML development and deployment.
Data modeling, Data governance Web performance optimization and mitigation strategies.

Nice To Haves

Master’s degree in engineering, Technology, or Computer Science
10–15 years of relevant industry experience in healthcare, pharmaceutical consulting, or enterprise data solutions.

Responsibilities

Execute the product and engineering roadmap for scalable platforms and products.
Define strategies for future development aligned with business and organizational objectives.
Lead technology discussions with senior leadership, providing insights and recommendations for strategic decisions.
Drive adoption of cutting-edge algorithms, machine learning techniques, and cloud-native architectures.
Design and develop data-driven platforms and AI/ML products to accelerate innovation.
Oversee requirements gathering, technical specifications, and Statements of Work (SOW).
Ensure compliance with security and regulatory standards.
Continuously review and improve engineering best practices and processes.
Build and lead a world-class engineering team, including data engineers, software developers, full-stack developers and technical leads.
Provide mentorship on software development lifecycle, architecture, big data analytics, AI/ML.
Establish and track key performance metrics for the engineering group.
Source and coordinate training to maintain technical excellence.
Partner with Product, Data Science, Sales, and IT to ensure alignment on priorities and deliverables.
Act as a trusted advisor to sales leadership and product management to support strategic wins.
Provide input on product enhancements and new development initiatives.
Manage departmental budgets and optimize cost efficiency.
Implement observability, incident response, and CI/CD best practices.
Champion agile methodologies and ensure high code velocity across distributed teams.