Senior Analytics Engineer

About the Role

Key Responsibilities

• Design and build scalable data pipelines using python, SQL and cloud-based tools (Azure, AWS).
• Develop and maintain analytics models and data transformation using dbt, airflow and data warehouses (ex: Snowflake, BigQuery).
• Build and manage dashboards and data visualizations using web-based tools, Tableau, Power BI.
• Ensure data quality, governance and security compliance across engineering workflows.
• Responsible for ingestion, integration and delivery of data products and insights across multiple platforms applying and maintaining data integrity and governance rules.
• Support adhoc analytics initiatives for clinical trials, commercial operations, and digital health systems.
• Develop high-quality analytical and statistical models, insights, patterns, and visualizations to improve decision-making in manufacturing operations.
• Document all technical work within and outside formal document management systems.
• Utilize supervised or unsupervised methods, learning from large amounts of unlabeled data to drive insights.
• Work with unstructured text data and deep learning methods for NLP.
• Apply knowledge of data science fundamentals such as causal inference, regression, classification, and experimental design.

Requirements

• Masters degree or PhD in Public Health, Economics, Statistics, Computer/Data Science, or related field.
• At least 5 years of experience in data engineering, analytics, or applied machine learning.
• Strong Python and SQL skills, with experience in ETL orchestration tools (Airflow, Prefect, Domino).
• Proficiency in modern data stack tools such as dbt, Snowflake/BigQuery, version control, CI/CD pipelines.
• Experience with DataOps/MLOps frameworks and AI/LLM tooling such as OpenAI, Langchain, Hugging Face Transformers.
• Strong communication and stakeholder engagement skills.
• Experience working in regulated industries such as pharma, biotech, or life sciences.
• Solid understanding of data science fundamentals including causal inference, regression, classification, and experimental design.
• Knowledge of cloud platforms (AWS, Azure), Spark, ERP, Salesforce data cloud, and data modeling in clinical trials or RWE is preferred.

Nice to Have

• Knowledge of GxP, HIPAA or compliance standards.
• Experience with data modeling in clinical trials, real-world evidence (RWE), or commercial analytics.
• Familiarity with NLP, time series forecasting, or image analytics in healthcare.

Benefits & Perks

• Pay rate of $80-$95 per hour.