Data Engineer

Index Analytics, LLC, is a rapidly growing Baltimore-based small business providing health related consulting services to the federal government. At the center of our company culture is a commitment to instilling a dynamic and employee-friendly place to work. We place a priority on promoting a supportive and collegial team environment and enhancing staff’s experience through career development and educational opportunities. 

Job Overview 

As a Data Engineer in this role, you will apply advanced data engineering principles to create reusable workflows that cover ingestion, quality assurance, transformation, and optimization of data. You will be responsible for building scalable pipelines using ETL tools, ensuring that data is efficiently extracted, transformed, and loaded into target systems. Your work will involve deploying robust data solutions into production environments and migrating information from legacy data warehouses by leveraging modern cloud architecture, particularly within Microsoft Azure SQL and Snowflake environments. Automating the flow of data for seamless consumption will be a key aspect of your daily responsibilities.

You will also play a pivotal role in establishing business processes that support strong data governance practices. This includes designing and advising on comprehensive data quality rules and setting up effective data compliance policies to ensure organizational standards are met. Exposure to ETL pipeline development, cloud-based data migration, and the implementation of data governance frameworks will be highly beneficial. Experience with optimizing data workflows, deploying solutions in cloud environments, and automating data processes will help you excel in this position. 

Responsibilities 

This role is centered on Snowflake as the primary data platform. Candidates are expected to demonstrate deep experience designing, developing, and optimizing enterprise-scale data solutions within Snowflake environments.

• Apply data engineering principles to develop reusable workflows for data ingestion, quality, transformation, and optimization
• Set up and maintain effective data compliance policies
• Build scalable data pipelines using ETL tools and processes
• Deploy data solutions to production environments
• Migrate data from legacy data warehouses leveraging cloud architecture principles
• Automate the flow of data for efficient consumption
• Build and implement business processes that enable data governance
• Design and advise on data quality rules