Staff - Data Engineer

Demonstrate foundational knowledge of credit card operations, banking, financial, loyalty rewards, retail, and credit card regulations while working with the business. Understand and employ common procedures and tools used in the Data Operations lifecycle (Agile Project Management, CI/CD, JIRA, etc.). Document important milestones and data lineage definitions. - (10%)

Provides guidance on building data pipeline for various data sources, particularly from many external data sources. Works with other project team members to understand the use cases, familiar with data sources, provides recommendation on data ingestion processes, complete data ingestion, monitor and identify potential process or data quality issues. - (15%)

Collaborate with Data Architects/Engineers on data model management and version control. Capture Critical Data Elements (CDE) and translate them into quality checks with Business/Data Engineers. Collaborate with business users, analysts, and architects to convert requirements into analytical tools and dashboards, aligning with usability best practices and design trends. - (10%)

Demonstrates analytical, interpersonal and professional communication skills. Learns quickly and works effectively individually and as part of a team. Accesses, extracts, and transforms Credit and Retail data from a variety of sources of all sizes (including client marketing databases, 2nd and 3rd party data) using Hadoop, Spark, SQL, Big data technologies etc. - (10%)

Provides automation help to analytical teams around data centric needs using orchestration tools, SQL and possibly other big data/cloud solutions for efficiency improvement. Supports Lead Data Engineer and Principal Data Engineer in new analytical proof of concepts and tool exploration projects. - (5%)

Effectively manages time and computing resources in order to deliver on time/correctly on concurrent projects. Involved in creating POCs to ingest and process streaming data using Spark and HDFS. Answers and trouble shoots questions about data sets and analytical tools; Develops, maintains and enhances new and existing analytics tools/Frameworks to support internal customers/consumers. - (10%)

Design, develop and implement data infrastructure and pipelines that collect, connect, centralize, and curate data from various internal and external data sources. Create automation systems and tools to configure, monitor, and orchestrate our data infrastructure and our data pipelines. - (10%)