Database Engineer

Founded in 2007, Initiate Government Solutions (IGS) is a Woman-Owned Small Business and a fully remote IT services provider supporting federal partners nationwide. We deliver innovative Enterprise IT and Health Services solutions with a strong focus on data analytics, health informatics, cloud migration, AI, and the modernization of federal information systems.

Our vision is to be a health IT trendsetter, continuing to solve the nation’s most challenging healthcare IT issues by conceiving, designing, and building solid, creative, and innovative open-source solutions.

Our mission is to innovate, design, and deliver tailored solutions that balance technical advancement with cost-awareness while providing exceptional service.

IGS is currently recruiting a remote Database Engineer to support our work with federal customer.

Assignment of Work and Travel:

This is a remote access assignment. The Candidate will work remotely daily and will remotely access IGS and customer systems and therein use approved IGS or customer provided communications systems. Travel is not required; however, the candidate may be required to attend onsite client meetings as requested.

In this role, you will collaborate with cross-functional teams—including product managers, engineers, security specialists, and business analysts—to deliver high-quality API products in alignment with federal standards and objectives. You will contribute to the program’s mission of improving veteran experiences by enabling digital innovation, ensuring compliance, and promoting best practices in API development and platform management.

Responsibilities and Duties (Included but not limited to):

• Database Architecture & Design: Design scalable, secure, and highly available cloud database solutions with emphasis on relational data modeling and schema design for large-scale migration initiatives. Evaluate and implement appropriate database technologies based on application requirements, including distributed SQL databases in hybrid cloud environments.
• Cloud Database Provisioning: Execute structured data migration pipelines across multi-environment workflows (dev-test, pre-production, production). Migrate on-premises relational database sources to cloud-based target platforms, ensuring data integrity, minimal downtime, and performance parity. Manage data staging through cloud object storage and coordinate pipeline progression through each environment.
• Performance Monitoring & Optimization: Monitor database performance using cloud-native and third-party tools. Identify and resolve bottlenecks in high-volume environments, including large-scale datasets with sub-second latency requirements. Implement indexing, partitioning, and query optimization strategies to meet performance SLAs.
• Security & Compliance: Implement access controls, encryption, and auditing. Ensure compliance with data protection regulations (e.g., HIPAA, HL7). Regularly review and update security policies.
• Backup & Disaster Recovery: Validate migrated data across pipeline stages to ensure accuracy, completeness, and structural integrity relative to source systems. Develop and execute test data strategies, including synthetic data generation that mirrors production structures without real PII/PHI.
• Automation & Scripting: Develop and maintain ETL notebooks and workflows for initial and incremental data loads. Automate data transformation, format conversion (e.g., raw to Delta Parquet), and pipeline orchestration. Develop scripts for synthetic data generation, routine maintenance, and monitoring using Python and infrastructure-as-code tools such as Terraform.
• ETL Pipeline Development: Build and maintain data pipelines using Databricks notebooks and Apache Spark for large-scale data processing. Manage data flow from source systems through cloud object storage staging into target database environments across dev-test, pre-production, and production tiers.
• Synthetic Data Generation: Generate structured test datasets that replicate production data schemas without containing real PII or PHI. Load synthetic data into containerized local database instances for initial pipeline validation prior to integration testing.
• Cost Optimization: Monitor and optimize database resource usage to control cloud costs. Recommend right-sizing and cost-saving measures.