ASD, Inc.
ASD, Inc.

Job Overview

ASD is seeking a Lead Data Engineer for the NCIS Program with GDIT in Quantico VA. This position will support the NCIS Office of the Command Data Officer (OCDO) and will be responsible for advancing the agencyrsquos mission of investigating and defeating criminal, terrorist, and foreign intelligence threats to the DON ndash in the maritime domain, ashore, and in cyberspace. The NCIS OCDO is committed to supporting the NCIS mission through the implementation of a modern, integrated internal data infrastructure with a focus primarily on exploiting the established data foundation to make internal and customer-centric business processes as effective and efficient as possible, laying the foundation for cognitive capabilities that can sense and respond to both internal and external customer data needs. The OCDO will also focus on expanding the organizationrsquos data ecosystem to include context-rich data while maintaining the agility needed to spark innovation. As and iatrical past of the OCDO, you will perform the following functions Responsibilities bull Establish shared operational data and integrated enterprise data, all while managing andor improving data quality and security through the creation of business-driven governance structures and culture change management. bull Establish data policies, standards, and procedures that improve data quality, availability, accessibility, security, usability, and enforcement of enterprise information management (EIM) program requirements. bull Establish enterprise standards ndash including a uniform and repeatable system development lifecycle methodology for Reference Data and Master Data (e.g., a common set of standards for data naming, abbreviations, and acronyms). bull Develop a data catalog to help NCIS organize and find data stored in their many systems. The data catalog shall include information about tables, files, and databases from the NCIS Enterprise Resource Planning (ERP), human resources (HR), finance, capability platforms, and social media feeds. The data catalog must also show where all the data entities are located. bull Develop a Master Data Management (MDM) Plan that focuses on the technology, tools, and processes ensuring master data is coordinated across the NCIS enterprise. MDM is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference. The data that is mastered may include reference data ndash the set of permissible values, and the analytical data supporting decision making. MDM provides a unified master data service intended to provide accurate, consistent and complete master data across the NCIS enterprise and to business partners. bull Recommend solutions based on performing industry-specific analysis, such as case studies describing data management best practices, identifying trends across the industry. bull Optimize and consolidate business and operational data while augmenting it with data about, and often generated by customers. Expand the data infrastructure to include sensor, device data, and other data sources. bull Make recommendations to improve the efficiency and effectiveness in how NCIS acquires, stores, manages, shares and applies its data. bull Engage business users and stakeholders for the increased release of actionable high-quality data on key operational and tactical activities at NCIS. bull Develop technology solutions to provide the platform, training, and standardized tools enabling querying, data mining, statistical analysis, reporting, scenario modeling, data visualization, and dash-boarding, and processes for a centralized, or analytics as a service model, allowing for the sharing of data across the enterprise from a common hub, facilitates cross-organizational data initiatives due to its enterprise-wide view of data assets and needs. bull Identify and manage risks proactively. Basic Qualifications bull Required Bachelor’s or master’s degree in computer science, statistics, applied mathematics, data management, information systems, information science or a related quantitative field or equivalent work experience. bull Preferred Advanced degree in computer science (MS), statistics, applied mathematics (Ph.D.), information science (MIS), data management, information systems, information science (post-graduation diploma or related) or a related quantitative field or equivalent work experience. bull Required Minimum six years or more of work experience in data management disciplines including data integration, modeling, optimization and data quality, andor other areas directly relevant to data engineering responsibilities and tasks. bull Strong experience with advanced analytics tools for Object-orientedobject function scripting using languages such as R, Python, Java, and C++. bull Strong ability to design, build and manage data pipelines for data structures encompassing o Data Transformation o Data Models o Schemas o Metadata o Workload Management bull The ability to work with both IT and business in integrating analytics and data science output into business processes and workflows. bull Strong experience with popular database programming languages including SQL and PLSQL for relational databases bull Preferred Certifications on upcoming NoSQLHadoop oriented databases like MongoDB, Cassandra are preferred but not required for non-relational databases. bull Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies. Including o ETLELT o Data ReplicationCDC o Message-oriented data movement o API design and access o Upcoming data ingestion and integration technologies such as stream data integration, CEP and data virtualization. bull Strong experience in working with SQL on Hadoop tools and experience working with technologies including HIVE, Impala, and Presto from an open source perspective and Hortonworks Data Flow (HDF), Dremio, Informatica, and Talend from a commercial vendor perspective. bull Strong experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production. bull Strong experience in working with both open-source and commercial message queuing technologies such as Kafka, JMS, Azure Service Bus, Amazon Simple queuing Service, others, stream data integration technologies such as Apache Nifi, Apache Beam, Apache Kafka Streams, and Amazon Kinesis, and stream analytics technologies such as Apache Kafka KSQL Apache Spark Streaming Apache and Samza. bull Basic experience working with popular data discovery, analytics and BI software tools to include Tableau, Qlik, and PowerBI (PowerBI is preferred) for semantic-layer-based data discovery. bull Strong experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms. bull Preferred Basic understanding of popular open-source and commercial data science platforms such as Python, R, KNIME, and Alteryx is a strong plus but not requiredcompulsory. bull Demonstrated success in working with large, heterogeneous datasets to extract business value using popular data preparation tools such as Trifacta, Paxata, and Unifi to reduce or even automate parts of the tedious data preparation tasks. bull Basic experience in working with data governance, data quality, and data security teams, data stewards, and privacy and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification. bull Demonstrated ability to work across multiple deployment environments including cloud, on-premises and hybrid environments, multiple operating systems and through containerization techniques such as Docker, Kubernetes, and AWS Elastic Container Service. bull Adept in agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse and automation of data flows between data managers and consumers across an organization bull Deep law enforcement (LE) information domain knowledge or previous experience working in LE would be a plus. bull Required Certifications as per Cybersecurity Workforce Management and Qualification Manual, SECNAV M-5239.2. bull Required SECRET w SSBILEAD DATA ENGINEER - secret 1

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