Senior Engineers, Data

What you'll do

• Design and implement scalable data architectures across on-premises and cloud environments using tools like Snowflake and Azure Data Lake.
• Develop end-to-end ETL/ELT pipelines using Python to ingest, cleanse, and transform data from diverse sources.
• Perform advanced performance tuning including SQL query refactoring, indexing, partitioning, and Spark job optimization.
• Build and maintain data governance frameworks, including validation rules, metadata management, and lineage tracking.
• Develop predictive analytics and machine learning solutions by preparing feature-engineered datasets and deployment pipelines.
• Implement DevOps best practices using GitLab for CI/CD pipeline configuration and automated testing of data workloads.
• Provide technical mentorship to team members regarding distributed processing, cloud engineering, and automation.
• Create technical documentation, architecture diagrams, and deployment standards to ensure operational continuity.

What we're looking for

• Must have a Master’s degree in Computer Engineering, Computer Science, or a related field and 3 years of relevant experience.
• Alternatively, must have a Bachelor’s degree in Computer Engineering, Computer Science, or a related field and 5 years of relevant experience.
• Experience designing and implementing scalable enterprise data platforms across on-premises, cloud, and hybrid environments using Microsoft Fabric, Azure Data Lake, Snowflake, PostgreSQL, and MS SQL Server.
• Experience using Azure Databricks for distributed data transformation and productionizing notebooks into automated jobs via CI/CD pipelines.
• Experience developing end-to-end ETL/ELT pipelines using Python-based scripting and distributed processing frameworks to integrate diverse data sources.
• Experience optimizing high-performance data models, SQL queries, indexing strategies, and partitioning for large-scale datasets.
• Experience building predictive analytics and machine learning solutions using Python and preparing feature-engineered datasets for model training and deployment.
• Authorized to work in the United States.