ETL Engineer

Overview

We are looking foraseasonedETLEngineer to work with our team and our clients to developenterprise grade data pipelines.We are looking for more than just an"ETLEngineer", but a technologist with excellent communication and customer service skills and a passion for data and problem solving.

Responsibilities

• Ability to obtain a government clearance, DOD Tier 3 clearance preferred
• Assess and understand ETL jobsandworkflows
• Create reusable data pipelines from source to target systems
• Test, validate, and deploy ETL pipelines
• Support reporting, business intelligence, and data science end users through ETL and ELT operations
• Work with data architects to create data models and design schemas for RDBMS, warehouse, and data lake systems
• Key must have skill sets – Python, SQL
• Work within an Agile software development lifecycle
• You will contribute to the growth of our Data Exploitation Practice!

Qualifications

• Required experience using Salesforce and Mulesoft
• Ability to hold a position of public trust with the US government.
• 2-4 years industry experience coding commercial software and a passion for solving complex problems.
• 2-4 years direct experience in Data Engineering with experience in tools such as:

• • ETL Tools: Python, Informatica, Pentaho, Talend
  • Big data tools: Hadoop, Spark, Kafka, etc.
  • Relational SQL and NoSQL databases, including Postgres and Cassandra.
  • Data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
  • AWS cloud services: EC2, EMR, RDS, Redshift (or Azure equivalents)
  • Data streaming systems: Storm, Spark-Streaming, etc.
  • Search tools: Solr, Lucene, Elasticsearch
  • Object-oriented/object function scripting languages: Python, Java, C++, Scala, etc
  • CompTIA Network + preferred.

• Advanced working SQL knowledge and experience working with relational databases, query authoring and optimization (SQL) as well as working familiarity with a variety of databases.
• Experience with message queuing, stream processing, and highly scalable ‘big data’ data stores.
• Experience manipulating structured and unstructured data for analysis
• Experience constructing complex queries to analyze results using databases or in a data processing development environment
• Experience working in an Agile environment
• Experience supporting project teams of developers and data scientists who build web-based interfaces, dashboards, reports, and analytics/machine learning models