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Exact Job
Location/Work Address
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Requirements are
scattered across 4 main locations: Richmond VA, McLean, VA, Wilmington,
DE, Chicago, IL
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Required
Technologies
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- Strong Programming
experience with object-oriented/object function scripting languages: Python, PySpark, Scala,
etc.
- Experience with big data
tools: Hadoop, Apache Spark, Kafka, etc.
- Experience with AWS
cloud services: S3, EC2, EMR, RDS, Redshift
- Experience with
stream-processing systems: Storm, Spark-Streaming, etc.
- Experience with
relational SQL, Snowflake and NoSQL databases, including Postgres and
Cassandra.
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Job
Description: Detailed overview of
functional and technical role expectations
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Candidate with 5+ years of experience in a Data Engineer role,
who has attained a Graduate degree in Computer Science, Statistics,
Informatics, Information Systems or another quantitative field. They should
also have working experience using the following software/tools:
- Strong Programming
experience with object-oriented/object function scripting languages:
Python, PySpark, Scala, etc.
- Experience with big data
tools: Hadoop, Apache Spark, Kafka, etc.
- Experience with AWS
cloud services: S3, EC2, EMR, RDS, Redshift
- Experience with
stream-processing systems: Storm, Spark-Streaming, etc.
- Experience with
relational SQL, Snowflake and NoSQL databases, including Postgres and
Cassandra.
Responsibilities for Data Engineer:
- Create and maintain
optimal data pipeline architecture, 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
loading of data from a wide variety of data sources using SQL and AWS
‘Big data’ technologies.
- Build analytics tools
that utilize the data pipeline to provide actionable insights into
customer acquisition, operational efficiency and other key business
performance metrics.
- Work with
stakeholders including the Executive, Product, Data and Design teams to
assist with data-related technical issues and support their data
infrastructure needs.
- Create data tools for
analytics and data scientist team members that assist them in building
and optimizing our product into an innovative industry leader.
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