Develop and maintain optimal data pipeline architecture.
Assemble large, complex data sets that meet functional and non-functional requirements of various business areas.
Identify, design, and implement internal process improvements, including automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and 'big data' technologies.
Build analytics tools that utilize the data pipeline to provide actionable insights into voting registration, execution, and results, operational efficiency, and other key business performance metrics.
Collaborate with stakeholders, including the Executive, Data, Design, and Support teams, to assist with data-related technical issues and support their data infrastructure needs.
Keep data separated and secure across Agency boundaries, such as data centers and cloud regions.
Create data tools for analytics and data scientist team members to assist them in building and optimizing data products needed to support ongoing operations and data-driven decision-making.
Work with data and analytics professionals to strive for greater functionality in our data systems.
Bachelor's Degree in Computer Science or related field of study, with a minimum of 10+ years of data/database background, including 5+ years acting as a Data Engineer.
2-3 years of cloud-based data services experience in AWS, such as ECT, Glue, EMR, RDS, and Redshift.
Real-time data streaming experience with technologies like Storm, Spark-Streaming, Kafka, or similar.
Advanced working knowledge of SQL and experience working with relational databases, including query authoring (SQL), as well as working familiarity with a variety of databases.
Experience building and optimizing 'big data' data pipelines, architectures, and data sets.
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Strong analytic skills related to working with unstructured datasets.
Build processes supporting data transformation, data structures, metadata, dependency, and workload management.
Successful history of manipulating, processing, and extracting value from large, disconnected datasets.
Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.
Strong project management and organizational skills.
Experience supporting and working with cross-functional teams in a dynamic environment.
5+ years of experience in a Data Engineer role, with a degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field.
Experience with big data tools such as Hadoop, Spark, Kafka, etc.
Experience with relational SQL and NoSQL databases, including Oracle, MS SQL Server, Postgres, Cassandra, etc.
Experience with data pipeline and workflow management tools such as Azkaban, Luigi, Airflow, etc.
Experience with data integration services solutions from vendors such as Informatica, MuleSoft, Talend, TIBCO, etc.
Experience with cloud-based data services such as AWS (EC2, Glue, EMR, RDS, Redshift, etc.).
Experience with stream-processing systems such as Storm, Spark-Streaming, Kafka, etc.
Experience with object-oriented/object function scripting languages such as Python, R, Java, C++, Scala, etc