Software Engineer (Data Engineer)

Ardent Principles, Inc.•Herndon, VA

2d•Onsite

About The Position

We’re looking for a motivated data engineer who enjoys building reliable pipelines and solving real‑world data challenges. In this role, you’ll help design and maintain production data workflows, write and optimize SQL, integrate data from SaaS platforms via APIs, and transform semi‑structured JSON/XML into clean, structured datasets. You’ll work with Python, cloud data services (AWS, Azure, or GCP), dimensional modeling concepts, and ETL/ELT patterns while contributing to scalable, well‑documented data infrastructure. Who We Are: We offer advanced services in data science, data engineering, software engineering, AI solutions, cybersecurity, staff augmentation, and IT program management. Passionate Integrity, Driven by Excellence At Ardent Principles, passionate integrity and a relentless pursuit of excellence define who we are. We offer a competitive salary range and a comprehensive benefits package designed to support your long‑term success. Our mission is to connect fulfilled employees with satisfied clients, ensuring your well‑being and professional satisfaction directly contribute to exceptional outcomes for those we serve. Join us as we continue building the future of secure, high‑impact solutions.

Requirements

Active TS/SCI with Full Scope Polygraph
Proficiency in Python for data engineering including working with data processing libraries.
Cloud data platforms such as AWS, Azure, or GCP, including data services and infrastructure.
Implementing ETL/ELT processes from diverse data sources.
Version control (Git) and software engineering best practices.
Strong problem-solving and troubleshooting skills for complex data pipeline issues.
Implementing data quality checks and validation frameworks.
Translating business requirements into technical data solutions.
Having a proven track record of delivering reliable, scalable data infrastructure.

Nice To Haves

ServiceNow APIs, data models, and integration patterns.
Network management or IT operations systems data extraction.
Forward Networks, NetIM, SolarWinds, or similar network management platforms.
Working with and knowledge of ITSM (Information Technology Service Management), ITOM (Information Technology Operations Management), and CMDB (Configuration Management Database) data structures and relationships.
API gateway platforms and API management tools.
Apache Spark, particularly PySpark, for distributed data processing.
DBT (data build tool) for transformation workflows.
Infrastructure-as-code tools such as Terraform or CloudFormation.
Implementing CI/CD (Continuous Integration / Continuous Delivery) pipelines for data engineering code.
Knowledge of streaming data technologies such as Kafka, Kinesis, or similar platforms.
Data quality platforms such as Great Expectations, Soda, or Monte Carlo.
Implementing data observability and monitoring solutions.
Knowledge of Data Vault or other advanced modeling methodologies.
Containerization (Docker) and orchestration (Kubernetes) for data workloads.
Reverse ETL and operational analytics patterns.
Data governance platforms and metadata management tools.
Multiple cloud platforms and multi-cloud architectures.
Mentoring or leading data engineering initiatives.

Responsibilities

Designing, building, and maintaining production data pipelines using orchestration tools such as Apache Airflow or similar.
SQL skills including complex queries, optimization, nd performance tuning across multiple database platforms.
Integrating data from Sponsor SaaS platforms and operational systems via APIs, including handling authentication, pagination, and rate limiting.
Working with semi-structured data (JSON and XML) from API responses and transforming into structured datasets.
Developing robust API integrations with proper error handling and retry logic.
Working with systems that have limited documentation or vendor-specific data models.
Working with dimensional modeling and data warehouse design patterns.
Proficiency in Python for data engineering including working with data processing libraries.
Cloud data platforms such as AWS, Azure, or GCP, including data services and infrastructure.
Implementing ETL/ELT processes from diverse data sources.
Version control (Git) and software engineering best practices.
Strong problem-solving and troubleshooting skills for complex data pipeline issues.
Implementing data quality checks and validation frameworks.
Translating business requirements into technical data solutions.
Having a proven track record of delivering reliable, scalable data infrastructure.