Data Engineer, Analytics

Design, model, and implement data warehousing activities to deliver the data foundation that drives impact through informed decision making. Design, build and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains. Collaborate with engineers, product managers and data scientists to understand data needs, representing key data insights visually in a meaningful way. Define and manage SLA for all data sets in allocated areas of ownership. Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve. Determine and implement the security model based on privacy requirements, confirm safeguards are followed, address data quality issues, and evolve governance processes within allocated areas of ownership. Solve challenging data integration problems utilizing optimal ETL patterns, frameworks, query techniques, and sourcing from structured and unstructured data sources. Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts. Influence product and cross-functional teams to identify data opportunities to drive impact. Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors. Demonstrate good judgment in selecting methods and techniques for obtaining solutions.

Master's degree ( or foreign degree equivalent) in Computer Science, Engineering, Information Systems, Mathematics, Statistics, Data Analytics, Applied Sciences, or a related field and three years of work experience in the job offered or in a data analytics or computer-related occupation.

Requires three years of experience in the following:

Features, design, and use-case scenarios across a big data ecosystem;

Custom ETL design, implementation, and maintenance;

Object-oriented programming languages;

Schema design and dimensional data modeling;

Writing SQL statements;

Analyzing data to identify deliverables, gaps, and inconsistencies;

Managing and communicating data warehouse plans to internal clients;

MapReduce or MPP system; and

Python.