Senior Data Engineer

Role Description

UST is seeking a highly skilled Senior Data Engineer with strong expertise in Python, PySpark, and AWS to design, build, and optimize scalable data pipelines and modern data platforms. The candidate will be responsible for delivering high-performance data solutions supporting analytics and business intelligence use cases in a cloud-native environment.

Opportunity

• Design, develop, and maintain scalable ETL/ELT pipelines using Python and PySpark for batch and streaming workloads.
• Build and manage data lake / lakehouse architectures (Bronze/Silver/Gold layers) to support enterprise analytics.
• Optimize Spark jobs for performance, scalability, and cost efficiency using best practices.
• Work with AWS services such as S3, Glue, EMR, Lambda, and Step Functions to integrate and orchestrate data workflows.
• Implement data quality, validation, and monitoring frameworks to ensure data reliability and integrity.
• Collaborate with cross-functional teams (Data, Platform, Application teams) to deliver end-to-end data solutions.

What You Need

• Core Experience:
  • 4+ years of experience in Data Engineering / Software Development.
  • Strong proficiency in Python programming (data structures, OOP, design patterns).
  • Hands-on experience in PySpark and Spark ecosystem including DataFrames, Spark SQL, and Spark Streaming.
  • Data Engineering & Processing Experience in building large-scale ETL/ELT pipelines.
  • Strong knowledge of data modeling and data lake architecture (Bronze/Silver/Gold).
  • Expertise with file formats such as Parquet, Avro, JSON.
  • Knowledge of data quality, validation, and governance practices.
  • Cloud (AWS) Expertise:
    • Hands-on experience with: AWS Glue (ETL jobs, Data Catalog, workflows)
    • Amazon EMR (cluster management, scaling, optimization)
    • AWS S3 (data lake design, partitioning, lifecycle)
    • AWS Lambda (serverless integrations)
    • Experience with orchestration tools: AWS Step Functions / Airflow (MWAA)
    • Monitoring via CloudWatch (logs, metrics, s)
  • Performance & Optimization:
    • Strong experience in Spark performance tuning and optimization.
    • Handling large datasets with focus on efficiency and cost optimization.
  • Development & DevOps Experience:
    • With CI/CD pipelines (CodePipeline, CodeBuild, CodeDeploy).
    • Familiarity with Infrastructure as Code (Terraform/CloudFormation).
    • Strong focus on sing and code quality (pys, unit/integration sing, PEP 8 standards).

Additional Skills

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder collaboration abilities.
• Ability to work independently and in Agile environments.

Pay & Benefits

Compensation can differ depending on factors including but not limited to the specific office location, role, skill set, education, and level of experience. UST provides a reasonable range of compensation for roles that may be hired in various U.S. markets as set forth below.

• Full-time, regular employees accrue a minimum of 10 days of paid vacation per year, receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year), 10 paid holidays, and are eligible for paid bereavement leave and jury duty.
• They are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching.
• They and their dependents residing in the US are eligible for medical, dental, and vision insurance, as well as the following Company-paid Employee Only benefits: basic life insurance, accidental death and disability insurance, and short- and long-term disability benefits.
• Benefits offerings vary in Puerto Rico.

Equal Employment Opportunity Statement

UST is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other applicable characteristics protected by law. We will consider qualified applicants with arrest or conviction records in accordance with state and local laws and “fair chance” ordinances.