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- 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 such as 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
- Keep 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 the product into an innovative industry leader
- Work with data and analytics experts to strive for greater functionality in our data systems
- 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
- Ability to 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
- 5+ years of experience in a Data Engineer role
- Graduate degree in computer science, statistics, informatics, information systems or another quantitative field
- Experience with big data tools: Hadoop, Spark, Kafka, etc.
- Experience with relational SQL and NoSQL databases, including Postgres and Cassandra
- Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
- Experience with AWS cloud services such as EC2, EMR, RDS, Redshift
- Experience with stream-processing systems such as Storm, Spark-Streaming, etc.
- Experience with object-oriented/object function scripting languages such as Python, Java, C, Scala, etc.