The Senior Data Engineer focuses on designing, implementing and supporting new and existing data solutions- data processing, and data sets to support various advanced analytical needs. You will be designing, building and supporting data pipelines consuming data from multiple different source systems and transforming it into valuable and insightful information. You will have the opportunity to contribute to end-to-end platform design for our cloud architecture and work multi-functionally with operations, data science and the business segments to build batch and real-time data solutions. The role will be part of a team supporting our Corporate, Sales, Marketing, and Consumer business lines.
MINIMUM QUALIFICATIONS AND REQUIREMENTS:
7+ years of relevant experience in one of the following areas: Data engineering, business intelligence or business analytics
5-7 years of supporting a large data platform and data pipelining
5+ years of experience in scripting languages like Python etc.
5+ years of experience with AWS services including S3, Redshift, EMR and RDS
5+ years of experience with Big Data Technologies (Hadoop, Hive, HBase, Pig, Spark, etc.)
Expertise in database design and architectural principles and methodologies
Experienced in Physical data modeling
Experienced in Logical data modeling
Technical expertise should include data models, database design and data mining
PRINCIPAL DUTIES AND RESPONSIBILITIES:
Design, implement, and support a platform providing access to large datasets
Create unified enterprise data models for analytics and reporting
Design and build robust and scalable data integration (ETL) pipelines using SQL, Python and Spark.
As part of Agile development team contribute to architecture, tools and development process improvements
Work in close collaboration with product management, peer system and software engineering teams to clarify requirements and translate them into robust, scalable, operable solutions that work well within the overall data architecture
Coordinate data models, data dictionaries, and other database documentation across multiple applications
Leads design reviews of data deliverables such as models, data flows, and data quality assessments
Promotes data modeling standardization, defines and drives adoption of the standards
Work with Data Management to establish governance processes around metadata to ensure an integrated definition of data for enterprise information, and to ensure the accuracy, validity, and reusability of metadata