Senior Data Engineer

Hyland

14d•Remote

About The Position

As a Senior Data Engineer at Hyland, you'll be at the heart of building the data foundation that powers enterprise-wide intelligence. You'll design and maintain scalable data pipelines, develop end-to-end analytical solutions, and work with large, diverse datasets to unlock meaningful insights. This is a high-impact individual contributor role for someone who brings deep technical expertise, thrives in a fast-paced environment, and is passionate about elevating the craft of data engineering across the team.

Requirements

Proven experience in data engineering, including working with data warehouses, ETL processes, and large-scale datasets from diverse sources.
Proficiency in SQL and Python, along with hands-on experience with at least one data processing framework such as Spark, Flink, TensorFlow, or PyTorch.
Experience with Data and ML pipeline orchestration tools (e.g., Metaflow, MLFlow, AWS Glue) and the ability to package and deploy code to production environments in the cloud.
Hands-on experience with AWS data platforms, including Terraform and Docker, with knowledge of CI/CD tools and experience applying generative AI for business purposes.
Excellent critical thinking, problem-solving, and collaboration skills with the ability to work independently, manage projects to completion, and thrive in a fast-paced, deadline-driven environment.

Nice To Haves

Experience with monitoring and observability tools such as Datadog, Grafana, or Prometheus.
Knowledge of machine learning concepts with proven real-world application experience, including hands-on familiarity with frameworks such as TensorFlow or PyTorch.
Demonstrated ability to influence, motivate, and mobilize team members and business partners across all levels of the organization.
Strong attention to detail with the ability to use original thinking to translate business goals into innovative, practical design solutions.
Experience mentoring and coaching team members and providing meaningful guidance to developing data engineering professionals.

Responsibilities

Design, build, and maintain scalable data pipelines to clean, transform, and prepare data for analysis and operational deployment across the enterprise.
Develop end-to-end solutions from data extraction through to operational deployment, working with large datasets from diverse sources to enable valuable insights and analytics.
Evaluate and enhance data and ML models; promote best practices for modeling and ensure scalable operational needs are met consistently across the team.
Ensure data storage and processing meet security standards and comply with relevant regulations; create and maintain thorough documentation for pipelines, architectures, and processes.
Operate as a trusted advisor and thought leader on data engineering trends and best practices; contribute significantly to the overall growth and quality of the department through knowledge sharing and coaching.
Mentor, coach, and provide timely feedback to team members; share insights with leadership on team capabilities and actively support the growth of developing data engineering professionals.