Senior Software Engineer, Data Engineering

5+ years of experience building, maintaining, and orchestrating scalable data pipelines. 3+ years of experience as a data engineer developing or maintaining integration with software such as Airflow or any Python-based data pipeline codebase. Experience applying a variety of integration patterns for different use cases. Experience in backend software development for distributed computing and data technologies. Experience implementing data pipelines and improving the performance of ETL processes and related SQL queries. Experience in data modeling for OLTP and OLAP applications. Experience with Cloud platforms such as Amazon AWS. Familiarity with workflow management tools (Airflow preferred). Familiarity with cloud-based data warehouses (Amazon Redshift preferred). Exceptional problem-solving and analytical skills. Experience working with sensitive data (PHI/PII) and security best practices. Familiarity with data governance practices and principles. Proficiency in SQL and experience with relational databases. Proficiency in Analytical SQL and experience with massive parallel processing (MPP) databases. Proficiency in programming languages such as Python, Java, or Scala. Knowledge of data modeling techniques (3NF) and tools. Software Engineering Mindset: Apply best practices to write elegant, maintainable code and understand automated testing concepts. Familiarity with business intelligence tools and environments. Familiarity with big data technologies (e.g., Lambda, Hadoop, Spark). Bachelor’s degree in Computer Science or a similar discipline preferred.

Design, develop, and implement scalable, secure, and efficient data solutions. Create and maintain logical and physical data models. Design, build, and optimize ETL processes and data pipelines. Integrate diverse data sources into a unified data platform. Monitor and optimize the performance of data systems and pipelines. Implement data quality checks, validation processes, and governance frameworks. Partner closely with data scientists, analysts, and other stakeholders. Maintain comprehensive documentation of data architectures, models, and pipelines. Train and collaborate with teammates on data engineering best practices. Recommend policy changes and establish department-wide procedures. Resolve complex problems using extensive experience and knowledge. Monitor and manage production environment to deliver data within defined SLAs. Evaluate, benchmark, and improve the scalability, robustness, and performance of data platform and applications. Make significant contributions to the architecture and design of the data processing platform. Implement scalable, fault-tolerant, and accurate ETL frameworks. Gather and process raw data at scale from diverse sources. Collaborate on technical vision, design, and planning. Implement and maintain a high level of data quality monitoring. Lead, document, and collaborate across teams for technical projects.