Senior Data Engineer - Build Scalable Data Pipelines

Experity; Rockford (IL); On-site; USD 110,000 - 150,000; Full time

Benefits offered by this job

Day-One Benefits

Ownership program after one year

Employee Assistance Program

Generous PTO and team events

Career growth programs

Competitive compensation with bonuses

Job summary

Experity, a mission-driven team transforming on-demand healthcare, seeks a Senior Data Engineer to design, build, and operate scalable data pipelines powering analytics, AI/ML, and healthcare products.

You will lead data engineering efforts, collaborate with Data Science, Machine Learning, and Product teams, and drive architecture decisions while mentoring peers in a fast-paced, remote-friendly environment.

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

Responsibilities

• 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.
• 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.
• 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.
• 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.

Skills

Python

SQL

Data engineering

Distributed data processing

Education

Bachelor's/Master's degree in CS/Engineering/IS

Tools

Snowflake

AWS

Kafka

Flink

Airflow

Docker

Kubernetes

Terraform

CloudFormation

dbt Cloud

Job description

Experity, a mission-driven team transforming on-demand healthcare, seeks a Senior Data Engineer to design, build, and operate scalable data pipelines powering analytics, AI/ML, and healthcare products.

You will lead data engineering efforts, collaborate with Data Science, Machine Learning, and Product teams, and drive architecture decisions while mentoring peers in a fast-paced, remote-friendly environment.