Data Engineer at Hearst Autos
Posted in Information Technology 30+ days ago.
This job brought to you by eQuest
Location: Chamblee, Georgia
We are looking for a Data Engineer to join our growing team of analytics experts. The hire 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 ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products.
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.
Qualifications for Data Engineer
- 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.
- 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.
Candidate should also have experience using the following software/tools:
- Experience with relational SQL and NoSQL databases, including Postgres, MongoDB, MySQL, SQL Server, etc..
- Experience with AWS cloud services: EC2, EMR, RDS, Redshift
- Experience with big data tools.
- Experience with data pipeline and workflow management tools
- Experience with stream-processing systems