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Position Overview

We are seeking a Senior Data Engineer to design, build, and operate scalable data pipelines and data platform capabilities that power Experity's analytics, AI/ML, and healthcare products.

This is a hands-on engineering role requiring strong expertise in data engineering, cloud technologies, distributed data processing, and software engineering. You will work closely with Data Engineering, Data Science, Machine Learning, Product, and Software Engineering teams to transform data from diverse sources into trusted, high-quality, and reusable data products.

As a senior member of the team, you will also provide technical leadership, mentor engineers, contribute to architecture and design decisions, and continuously improve the scalability, reliability, and efficiency of Experity's data ecosystem.

What You'll Do

Data Engineering & Platform

• Design, build, and maintain scalable ETL/ELT pipelines for batch and near-real-time data processing.

• Integrate data from databases, APIs, applications, event streams, and external sources into Experity's enterprise data platform.

• Build reusable, high-quality data models and curated data products for analytics, reporting, AI/ML, and operational applications.

• Develop complex data transformations and processing workflows using Python, SQL, Snowflake, and distributed data technologies.

• Design data solutions for scalability, availability, resiliency, performance, and cost efficiency.

Engineering Excellence & DataOps

• Develop reusable frameworks, libraries, and engineering patterns that improve developer productivity and data pipeline consistency.

• Implement automated testing, CI/CD, Infrastructure as Code, and DataOps practices across data engineering workflows.

• Build monitoring, alerting, data observability, and automated remediation capabilities to ensure pipeline reliability and data integrity.

• Troubleshoot complex production issues and drive root-cause analysis and long-term improvements.

• Continuously optimize data pipelines, queries, storage, and compute for performance and cost.

Data Quality, Governance & Security

• Implement data quality, validation, lineage, and reconciliation controls across critical data pipelines.

• Partner with Data Architecture, Governance, and Security teams to ensure data solutions follow enterprise standards.

• Ensure healthcare data is handled securely and in accordance with applicable privacy, security, and compliance requirements.

Technical Leadership & Collaboration

• Provide technical leadership through solution design, architecture discussions, code reviews, and engineering best practices.

• Mentor Data Engineers and help improve engineering quality and technical capabilities across the team.

• Partner with Data Scientists and Machine Learning Engineers to build reliable datasets, feature pipelines, and data infrastructure for AI/ML applications.

• Collaborate with Product, Engineering, Analytics, and business stakeholders to translate requirements into scalable data solutions.

• Maintain clear technical documentation for data pipelines, models, architecture, and operational processes.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field.

• 5+ years of experience building production-grade data pipelines, data platforms, and large-scale data processing solutions.

• Advanced proficiency in Python and SQL with strong software engineering fundamentals.

• Hands-on experience with Snowflake and cloud-based data platforms.

• Experience with Kafka,Flink, or other distributed and streaming technologies.

• Strong experience with AWS, including services such as S3, Lambda, Glue, Kinesis, or equivalent cloud technologies.

• Experience designing and optimizing ETL/ELT pipelines, data models, and high-volume data processing workflows.

• Experience with orchestration technologies such as Airflow or equivalent platforms.

• Experience with CI/CD, Git, automated testing, and Infrastructure as Code.

• Strong understanding of data quality, governance, security, observability, and production support.

• Strong problem-solving, communication, and cross-functional collaboration skills.

• A "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end

Preferred

• Experience transforming data leveraging dbt, preferably dbt cloud.

• Experience working in AI-native engineering environments and effectively leveraging AI-assisted development tools to improve engineering velocity, code quality, and operational efficiency.

• Experience supporting Machine Learning and Generative AI workloads, including feature engineering and ML data pipelines.

• Experience with Docker, Kubernetes, Terraform, or CloudFormation.

• Experience with data cataloging, lineage, metadata management, and data observability platforms.

• Experience in Healthcare, HealthTech, SaaS, or other regulated industries.

Why our team?

• Build scalable data platforms that power healthcare products, analytics, Machine Learning, and Generative AI.

• Solve challenging data problems involving large, complex, and highly valuable healthcare datasets.

• Work with modern technologies including Snowflake, AWS, Python, Spark, and streaming platforms.

• Collaborate closely with Data Engineering, Machine Learning, Data Science, Product, and Engineering teams.

• Provide technical leadership while continuing to remain deeply hands-on with engineering.

Experity is committed to fostering a diverse, equitable, and inclusive workplace where innovation thrives through collaboration, diverse perspectives, and continuous learning.

Equal Opportunity Employer
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