Data Architect

The Sponsor requires Data Engineering support to evaluate, optimize, and implement robust data infrastructure that enables reliable, accessible, and scalable data delivery across the organization. The Contractor will work collaboratively with data consumers, technical teams, leadership, and stakeholders to assess current data pipelines, identify gaps in data accessibility and reliability, and architect solutions that establish trusted data foundations. Work involves applying engineering best practices to implement proper data modeling and integration patterns, ensuring data quality and observability throughout pipelines, and creating maintainable infrastructure that supports analytics, reporting, and operational use cases.

The Sponsor’s data landscape includes enterprise operational systems such as Service Now, network management platforms (NetIM), and network modeling tools (Forward Networks). The Data Engineering support must be adept at extracting data from these systems via APIs, exports, and vendor‑specific interfaces, often with limited documentation or non‑standard data structures, and transforming this operational data into accessible, integrated datasets.

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

• Demonstrated experience with Service Now APIs, data models, and integration patterns.
• Demonstrated experience with network management or IT operations systems data extraction.
• Demonstrated experience with Forward Networks, NetIM, Solar Winds, or similar network management platforms.
• Demonstrated experience and knowledge of ITSM, ITOM, and CMDB data structures and relationships.
• Demonstrated experience with API gateway platforms and API management tools.
• Demonstrated experience with Apache Spark, particularly PySpark, for distributed data processing.
• Demonstrated experience with DBT (data build tool) for transformation workflows.
• Demonstrated experience with infrastructure‑as‑code tools such as Terraform or Cloud Formation.
• Demonstrated experience implementing CI/CD pipelines for data engineering code.
• Demonstrated experience and knowledge of streaming data technologies such as Kafka, Kinesis, or similar platforms.
• Demonstrated experience with data quality platforms such as Great Expectations, Soda, or Monte Carlo.
• Demonstrated experience implementing data observability and monitoring solutions.
• Demonstrated experience and knowledge of Data Vault or other advanced modeling methodologies.
• Demonstrated…