Data Engineer

Responsibilities:

• Designed, developed, and coded large scale enterprise-class computer processing systems for data dissemination, including managing the ongoing development/maintenance.
• Monitoring, performance enhancements, optimization, integration, high availability, and documentation of enterprise-class systems for data dissemination.
• Analyzed and defined customer requirements.
• Create documentation, diagrams, processes, and procedures architected data dissemination tools, services, and frameworks.
• Experience with data extraction, translation, and loading including data prep and labeling to enable data analytics.
• Experience with Kibana and Elasticsearch.
• Familiarity with various log formats such as JSON, XML, and others.
• Experience with data flow, management, and storage solutions.
• Ability to decompose technical problems and troubleshoot system and dataflow issues.
• Leverage knowledge and experience with metadata management, including defining and incorporating metadata standards for business, operational, and technical metadata.
• Apply knowledge and experience with data storage technologies, including relational databases, NoSQL databases, and big data platforms.
• Interpret and implement Federally mandated policies, procedures, and other directives to meet agency compliance requirements.

Qualifications:

• 7 years of experience as a Database Developer.
• 3 years of experience as a hands on Team Lead.
• Experience designing and delivering large scale, 24-7, mission-critical data pipelines and features using modern big data architecture.
• 5-7 years of experience with SQL Server On-Prem and/or Azure cloud, required.
• 5-7 years of SSIS experience utilizing SQL Agent jobs and Integration Services Catalog, required.
• 3-4 years of experience running ETL/ELT SSIS projects independently from end to end, required.
• Experience with DevOps Repository and Git, preferred.
• Azure Data Lake storage experience, a plus.
• Azure Data Factory and/or Databricks experience, a plus.

Key Skills:

• Data Warehousing.
• Data Architecture. 
• Coding. 
• Operating System. 
• Apache Hadoop-Based Analytics. 
• Machine Learning.