OverviewSenior Data Engineer
📍 Brazil | 🌐 Remote
About the Role
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.
Your Tools
- SQL & Python
- Apache Spark / Flink
- TensorFlow / PyTorch
- Metaflow / MLFlow / AWS Glue
- AWS Data Platform
- Terraform & Docker
- Datadog / Grafana / Prometheus
- CI/CD Tools
Your Role Responsibilities – Here's What You'll Do
- 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.
Role Essentials
You'll need these skills to hit the ground running:
- 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.
What We'd Like to See
These will help you stand out:
- 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.
