Cloud Data Engineer - Remote, United States - Leidos

Description

Description

Looking for an opportunity to make an impact?

Leidos is seeking a seeking a customer experience focused MicrosoftAzureCloudData Engineer to work with a team of subject matter experts and developers to design and implement full lifecycle data pipeline services for Azure cloud based data lake, SQL, and NoSQL data stores. As a data engineer, you will translate business requirements to data engineering solutions to support an enterprise scale Microsoft Azure based data analytics and reporting platform. Our ideal candidate is mission focused and delivery oriented, and applies critical thinking to create innovative functions and solve technical issues.

Who we are

Leidos is a Fortune 500 technology, engineering, and science solutions and services leader working to solve the world's toughest challenges in the defense, intelligence, civil, and health markets. Leidos Digital Modernization group helps the government modernize operations with leading edge AI/ML driven data management and analytics solutions. We are a trusted partner to both government and highly-regulated commercial customers looking for transformative solutions in mission IT, security, software, engineering, and operations. We work with our customers including the FAA, DOE, DOJ, NASA, National Science Foundation, Transportation Security Administration, Custom and Border Protection, airports, and electric utilities to make the world safer, healthier, and more efficient.

In this role, you will:

• Support the development, lifecycle management, and deployment of data pipeline services and solutions.
• Work with client personnel and team members to understand data requirements and implement appropriate data solutions.
• Design and implement data models and data pipelines for relational, dimensional, data lakehouse (medallion architecture), data warehouse, data mart, NoSQL data stores.
• Manage and optimize data storage using Azure ADLS Gen2, Azure Synapse SQL Pools, Azure Cosmos DB.
• Develop batch/incremental/streaming data pipelines using Python, SQL, Spark, Scala and Microsoft Azure services including ADLS Gen2/Blob Storage, Data Factory, Synapse Pipelines, Azure Analysis Services, Logic Apps, Azure Functions, Azure Files, Visual Studio, SQL Server Change Data Capture, SQL Server stored procedures.
• Redevelop or migrate existing SSIS extract, transform, load scripts to Azure Data Factory.
• Identify, create, prepare data required for advanced analytics, visualization, reporting, and AI/ML.
• Implement data migration, data integrity, data quality, metadata management, and data security functions, and apply advanced techniques such as machine learning to optimize data pipelines.
• Monitor and troubleshoot data related issues to maintain high availability and performance.
• Implement governance, build, deployment and monitoring standards to automate platform administration.
• Apply DevOps and CI/CD principles to development, test, deployment, and release of code.

For this position, you must possess:

• BS degree in Computer Science or related field and 8+ years or Masters with 6+ years of experience
• 4+ years of professional experience implementing Azure cloud based data engineering solutions
• 4+ years of experience designing and building solutions utilizing various cloud services such as Azure Blob, Azure Data Lake Services, Azure Synapse Analytics, Azure Data Factory, Integration Runtime, Azure EventHubs, Azure Functions, Azure Logic Apps, Azure Analysis Services
• 4+ years of experience in the design and build of ETL/ELT processes by writing custom data pipelines, initial, incremental and change data capture experiences
• 4+ years of experience with more than one of the follow scripting languages: SQL, T-SQL, Python, Scala, PySpark
• Experience working with Microsoft database and business intelligence tools, including SQL Server, including stored procedures, SSIS, SSRS, SSAS (cubes)
• Knowledge and experience with shell scripting, MDX, DAX queries
• Admin, system level experiences with data management, DB creation, user management/access control, ETL package deployment, data modeling, scheduling, debug, monitor, security controls, and O&M aspect for both on-premise and cloud based data assets.
• Experience with data engineering solutions for data warehouse, data mart, multi-dimensional models, semantic data models (e.g. Azure Analysis Services)
• Knowledge and understanding of AWS cloud services including data management and data movement services.
• Demonstrated experience in supporting a production, testing, integration, and development environments
• Open mindset, ability to quickly adapt new technologies to solve customer problems
• Experience with Agile process methodology, and CI/CD automation, and cloud based development (Azure, AWS).
• Ability to successfully obtain a government-issued Public Trust clearance.
• Must be a US Citizen

Not required, but additional education, certifications, and/or experience are a plus:

• Experience with ETL/ELT processes for Master Data Management.
• Working within Scaled Agile Framework (SAFe) process.
• Experience working with Azure DevOps.
• Knowledge and understanding in data governance, data discovery tools such as Alation, MS Purview
• Knowledge and experience of configuring ETL pipelines in cloud, VPC/VNet config, Integration Runtime, Gateway, EC2/Bastion access settings
• Microsoft certified in Azure fundamentals, data engineer, AI or AWS certified data engineer.

Salary range for this position is $120K - $128K

Original Posting Date:

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.

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