About the Role:

We are seeking a Lead Product AI Data Engineer to take ownership of the development of data engineering and AI capabilities within our healthcare-focused product ecosystem.

The role is highly hands-on and suited for an engineer who can independently design and develop scalable data pipelines while contributing to AI-enabled features, machine learning workflows, and intelligent data applications.

You will collaborate with product, analytics, data science, and engineering teams to convert complex datasets into reliable product capabilities.

Key Responsibilities:

- Develop and maintain scalable data pipelines supporting AI-enabled healthcare and medical device products.

- Build data ingestion, transformation, processing, and integration workflows using Python and PySpark.

- Develop ETL/ELT pipelines and workflow orchestration using Apache Airflow.

- Implement data processing and storage solutions using Databricks, Snowflake, and Delta Lake.

- Write optimized SQL queries and develop data models for analytical and product workloads.

- Create reusable data pipelines for machine learning and AI use cases.

- Work with Data Scientists to prepare training datasets, implement feature engineering workflows, and integrate ML models into production applications.

- Support the implementation of Generative AI features using LLMs, RAG, embeddings, vector databases, and AI APIs.

- Contribute to the development of AI agents and intelligent workflows for data-driven product use cases.

- Build data preparation and retrieval workflows for enterprise AI applications.

- Implement data validation, quality checks, monitoring, and pipeline observability.

- Troubleshoot pipeline failures, data issues, performance bottlenecks, and production incidents.

- Deploy and operate data workloads on AWS or Azure cloud environments.

- Participate in technical design and architecture discussions and contribute practical implementation recommendations.

- Conduct code reviews and promote clean, maintainable, and scalable engineering practices.

- Work closely with Product Managers, Data Scientists, Analysts, and other engineers to understand product requirements and deliver solutions.

Technical Skills:

- Strong hands-on programming experience in Python.

- Good experience with PySpark and distributed data processing.

- Hands-on experience with Snowflake, Databricks, and Delta Lake.

- Experience building and managing Airflow workflows.

- Strong SQL skills and experience with databases such as PostgreSQL, Oracle, Snowflake, or Databricks.

- Working knowledge of AWS or Azure cloud platforms.

- Understanding of data engineering concepts including ETL/ELT, data modeling, data quality, pipeline optimization, and distributed processing.

- Exposure to Generative AI, LLMs, RAG, vector databases, embeddings, or AI agents.

- Understanding of machine learning data pipelines and basic MLOps concepts.

- Exposure to specification-driven development approaches such as SpecKit or OpenSpec is an advantage.

Preferred Domain Experience:

- Experience with Healthcare, Life Sciences, Pharmaceutical, Biotechnology, or Medical Device data is desirable.

- Awareness of data privacy, security, governance, and regulatory considerations is an advantage.

Qualifications:

- Bachelor's degree or equivalent qualification in Computer Science, Software Engineering, Data Engineering, Artificial Intelligence, or a related discipline.

- 5 - 7 years of experience in data engineering, software engineering, AI engineering, or related technical roles.

- Strong problem-solving skills with the ability to independently own technical deliverables.

- Good communication and collaboration skills with cross-functional teams.

Success in this Role:

- Success will be measured by the quality and reliability of data pipelines, successful delivery of AI-enabled product features, pipeline performance, production stability, engineering efficiency, and the ability to translate product requirements into scalable technical solutions.