Data Engineer | Python  

Responsibilities

• Will be responsible for expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross-functional teams.
• The Data Engineer will support our software developers, database architects, data analysts, and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects.
• They must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products.
• The right candidate will be excited by the prospect of optimizing or even re-designing our company’s data architecture to support our next generation of products and data initiatives.
• 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, and 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.
• Keep our data separated and secure across national boundaries through multiple data centers and AWS regions.
• Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Work with data and analytics experts to strive for greater functionality in our data systems.

Qualifications

• 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.

Proven experience using the following software/tools:

○ Experience with big data tools: Hadoop, Spark, etc.
○ Experience with relational SQL and NoSQL databases(optional), including Postgres and Cassandra(optional).
○ Experience with AWS cloud services: EC2, RDS, Redshift, Lambda, Athena, Cloudwatch.
○ Experience with stream-processing systems: Storm, Spark-Streaming, etc.
○ Experience with object-oriented/object-function scripting languages: Python, Bash, etc.

• Advanced working SQL knowledge and experience working with relational databases, 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.
• A 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.
• Candidates must be experienced in using python programming language and knowledgeable in using some of AWS Services for Big Data Processing mentioned below.
• The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up.