Overview
Remote
On Site
Full Time
Skills
Python
Hadoop
Data engineer
Spark
Scala
airflow
Job Details
Primary Responsibilities:
- Create and maintain optimal data pipeline architecture
- Build data pipelines that transform raw, unstructured data into formats that data analyst can use to for analysis
- Assemble large, complex data sets that meet functional / non-functional business requirements
- Identify, design, and implement internal process improvements:
automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and delivery of data from a wide variety of data sources using SQL and AWS Big Data technologies
- Work with stakeholders including the Executive, Product, Engineering, and program teams to assist with data-related technical issues and support their data infrastructure needs.
- Develop and maintain scalable data pipelines and builds out new integrations and processes required for optimal extraction, transformation, and loading of data from a wide variety of data sources using scalable distributed Data technologies
- Implements processes and systems to validate data, monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it
Primary Qualifications:
- 6+ yrs experience and bachelor's degree in computer science, Informatics, Information Systems or a related field; or equivalent work experience
- In-depth working experience of distributed systems Hadoop/MapReduce, Spark, Hive, Kafka and Oozie/Airflow
- At least 5 years of solid production quality coding experience in data pipeline implementation in Java, Scala and Python
- Experience with AWS cloud services: EC2, EMR, RDS
- Experience in GIT, JIRA, Jenkins, Shell scripting
Nice to have skills:
- Experience building Marketing Data pipelines including Direct Mail will be a big plus
- Experience with Snowflake and Salesforce Marketing Cloud
- Working knowledge of open-source ML frameworks and end-to-end model development life cycle
- Previous working experience with running containers (Docker/LXC) in a production environment using one of the container orchestration services (Kubernetes, AWS ECS, AWS EKS)
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.