DataOps Engineer

DataOps Engineer Requirements: Bachelor's degree in Computer Science, Computer Engineering, or a related technical degree; four years related experience; or equivalent combination of education and experience 2+ years experience in data streaming technologies, such as Kafka 2+ years experience using ETL (Extract, Transform, and Load) concepts Experience with querying and designing databases using one or more of the following: MySQL, MS SQL, Oracle SQL, or other professional database system Ability to work in teams and collaborate with others to clarify requirements, quickly identify problems, and collaboratively find creative solutions Ability to assist in documenting requirements as well as resolve conflicts or ambiguities Nice to Have Skills: 4 or more years experience in programming using one or more of the following: Java, C++, Perl, Python, or advanced Shell scripting. 3 or more years of experience in implementing data-driven solutions using tools such as Hadoop, Impala, Hive, NiFi, Athena, Redshift, BigTable, or Airflow. 2 or more years experience in machine learning and statistical modeling Experience in Cloud Native tools, such as Kubernetes and Docker Experience with using the R statistical computing language Experience with Agile at Scale, SAFe, and Lean Systems Engineering DataOps Engineer Responsibilities: Develop high-volume, low-latency, data-driven solutions utilizing current and next generation technologies to meet evolving business needs Acquire big data input from numerous partners. Key technologies may include Python, Airflow, Prometheus, and Kafka. Normalize complicated data sources to convert potentially unusable data into a format that can be efficiently used by software and/or employees. Key technologies may include Spark, Kinesis, Lambda Build a CI/CD pipeline for our data software to ensure we keep quality high and time to market low. Key technologies may include Gitlab.