Data Software Engineer

Position Overview

The Data Software Engineer plays a key role in building and evolving a modern enterprise data platform in a cloud-first environment. This role partners with analysts, data scientists, software engineers, and business stakeholders to deliver trusted, secure, and high-quality data solutions that power analytics, operational reporting, and emerging AI use cases. The position offers the opportunity to work across Snowflake, AWS, and modern engineering tooling to develop scalable pipelines, reusable platform capabilities, and reliable production processes with strong emphasis on automation, observability, and continuous improvement.

Essential Duties And Responsibilities

• Partner with business and technology teams to turn data, reporting, and platform needs into scalable solutions and prioritized engineering work
• Design, build, and support batch and near-real-time data pipelines, data products, and integration workflows using Snowflake, AWS, and approved enterprise technologies
• Write clean, maintainable code in SQL, Python, and related languages while following engineering standards, code review practices, and automated testing
• Develop data transformation, modeling, and orchestration patterns that improve usability, consistency, performance, and speed to delivery
• Strengthen platform reliability through monitoring, alerting, logging, data quality checks, and effective production support
• Contribute to CI/CD, infrastructure automation, and environment management practices that improve delivery speed, consistency, and security
• Apply governance, lineage, security, and privacy standards throughout the data development lifecycle
• Create clear technical documentation, operational runbooks, and engineering standards that improve maintainability and team knowledge sharing
• Collaborate across platform, application, analytics, and product teams to expand reusable components and advance data platform maturity
• Perform other duties as assigned

Qualification Requirements

• Must be at least 21 years of age
• Bachelor’s degree in computer science, information systems, engineering, or a related field, or equivalent practical experience
• Three (3)+ years of experience in data engineering, software engineering, analytics engineering, or a related technical discipline
• Hands-on experience building and supporting production data pipelines, integration workflows, and data transformations using SQL and Python
• Experience with cloud-native data platforms and services, preferably AWS and Snowflake
• Understanding of data modeling, ETL/ELT patterns, orchestration, and data lifecycle management for analytics and operational use cases
• Working knowledge of modern software delivery practices, including version control, code review, testing, CI/CD, and Agile methods
• Ability to troubleshoot and support production workloads with attention to reliability, performance, and root cause analysis
• Understanding of data quality, governance, lineage, security, and privacy principles in enterprise environments
• Strong analytical, problem-solving, and communication skills, with the ability to work effectively with technical and non-technical stakeholders
• Willingness and ability to work a flexible schedule to include holidays, nights, and weekends
• Work in a fast-paced, busy, and somewhat stressful environment

Preferred Skills

• Experience delivering modern data engineering solutions in Snowflake- and AWS-based environments that support analytics, operational reporting, or AI/ML use cases
• Experience with transformation and orchestration tools such as dbt, Airflow, or comparable enterprise workflow platforms
• Experience with AWS services such as S3, Lambda, EC2, CloudWatch, containerized workloads, or other cloud-native compute and monitoring services
• Knowledge of Snowflake capabilities such as Snowpipe, task-based orchestration, performance optimization, data sharing, and governance features
• Experience with data quality, observability, lineage, metadata, or cataloging practices in production environments
• Knowledge of infrastructure as code, automated deployment practices, and environment standardization for data platforms
• Experience with streaming, event-driven, or near-real-time data patterns
• Strong engineering fundamentals, including data modeling, system design, testing, performance tuning, and secure software development practices