Data Engineer

SAIC • Washington, DC

9d

About The Position

SAIC is seeking a hands-on Data Engineer with strong Azure experience to build and maintain reliable, scalable data pipelines and datasets that power analytics and operational insights. You will own ingestion, transformation, and data product delivery across Azure services, with Azure Data Factory (ADF), Blob Storage, and Azure SQL as core technologies. Experience with Azure Synapse Analytics, Microsoft Fabric, and Power BI is a plus. You will design, develop, and operate data solutions using multiple data modeling approaches (e.g., star/snowflake, Data Vault) and data formats (e.g., JSON, Parquet, CSV, Avro). You will collaborate closely with analytics, product, and operations teams to deliver trusted, secure, and performant data products in a modern Azure-based data platform.

Requirements

• BS and 5 years experience (4 years experience in lieu of degree)
• Ability to obtain and maintain a public trust requiring U.S. Citizenship.
• Hands-on experience with Azure Data Factory (ADF): pipelines, data flows, linked services, triggers, parameterization.
• Strong experience with Azure Blob Storage: containers, access tiers, lifecycle management, SAS/Managed Identities.
• Strong experience with Azure SQL (Database/Managed Instance): schema design, performance tuning, indexing, query optimization.
• Practical experience with star and snowflake schemas, 3NF, and Data Vault concepts.
• Understanding of Kimball vs. Inmon data warehousing approaches.
• Proficiency with JSON, Parquet, CSV, Avro.
• Experience handling schema evolution and versioning for these formats.
• Strong SQL skills for complex querying and transformations.
• Scripting experience in Python (preferred) or Scala for data transformations, utilities, and automation.
• Experience with Git-based workflows.
• Experience integrating data pipelines with Azure DevOps (Repos, Pipelines) or equivalent CI/CD tools.
• Proven experience with monitoring, logging, alerting, and troubleshooting data pipelines in production environments.

Nice To Haves

• Experience with Azure Synapse Analytics: serverless and/or dedicated SQL pools, data warehouse patterns, and Spark notebooks for large-scale ELT/ETL.
• Familiarity with Microsoft Fabric: Lakehouse, OneLake, notebooks, data pipelines, and workspace governance.
• Experience building Power BI semantic models (datasets), incremental refresh, relationships, calculation groups, and DAX for data product delivery.
• Experience with Event Hubs, Azure Functions, or Databricks/Spark Structured Streaming for real-time or near real-time data ingestion.
• Experience using Bicep or Terraform to provision and manage Azure resources; familiarity with ARM templates.
• Experience with Microsoft Purview or similar tools for data cataloging, lineage, and metadata management.

Responsibilities

• Design, develop, schedule, and monitor batch and near real-time data pipelines in Azure Data Factory (ADF) for data ingestion, transformation, and load (ELT/ETL).
• Implement and manage data storage strategies in Azure Blob Storage (access tiers, lifecycle policies, partitioning, folder design).
• Design, manage, and optimize datasets in Azure SQL (Database/Managed Instance) including schema design, indexing, and performance tuning.
• Create and maintain dimensional models (star/snowflake), and apply 3NF, normalization/denormalization, and Data Vault 2.0 patterns where appropriate.
• Implement data quality and governance capabilities including validation rules, profiling, lineage, documentation, and standards for schema evolution and data contracts.
• Optimize pipeline runtimes, SQL queries, and storage I/O; implement alerting, retry logic, and cost controls to ensure performance and reliability.
• Collaborate with analytics, product, and operations teams to understand requirements and deliver robust, well-documented datasets and data products.
• Apply security and compliance best practices, including RBAC, data masking, encryption at rest/in transit, and key management, in line with company policies.
• Contribute to best practices, templates, and reusable components to improve consistency and speed of delivery across the data platform.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level