Data Engineer, Sustaining Product Integrity

Job summary
Do you enjoy solving complex problems and driving change? Do you thrive in a fast-paced environment? Our team of Failure Analysis Engineers at Ring is tasked with solving systemic hardware issues and building tools to detect and mitigate any future recurrences. We work closely with development engineers and vendors to get to the root cause and drive changes back into our products. We are also responsible for driving changes back into development processes and behavior specifications.

Data Engineer roles have a huge impact on the day to day productivity of internal teams through building infrastructure to answer questions with data, using software engineering best practices, data management fundamentals, and recent advances in distributed systems (i.e. MapReduce, noSQL databases). This role requires partnership with passionate scientists, business intelligence engineers, software development engineers and product managers, to deliver a variety of stable and performant data feeds used for developing business insights as well as offline machine learning use cases.

Key job responsibilities

• Contribute to the architecture, design and implementation of next generation BI solutions – including streaming data applications
• Manage AWS resources including EC2, RDS, Redshift, Kinesis, EMR, Lambda etc
• Collaborate with data scientists, BIEs and BAs to deliver high quality data architecture and pipelines.
• Interface with other technology teams to extract, transform, and load data from a wide variety of data sources
• Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers

Basic Qualifications

• 3+ years of experience as a Data Engineer or in a similar role
• Experience with data modeling, data warehousing, and building ETL pipelines
• Experience in SQL
• Industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets
• Experience using big data technologies (Hadoop, Hive, Hbase, Spark, EMR, etc.)
• Experience working with AWS big data technologies (EMR, Redshift, S3, AWS Glue, Kinesis and Lambda for Serverless ETL)
• Knowledge of data management fundamentals and data storage principles
• Knowledge of distributed systems as it pertains to data storage and computing
• Hands-on experience and advanced knowledge of SQL
• Basic scripting skills using Python and Scala
• Basic understanding of Machine Learning

Preferred Qualifications

• 5+ years of experience as a Data Engineer, BI Engineer, Business/Financial Analyst or Systems Analyst in a company with large, complex data sources
• Experience with electrical engineering or hardware triage concepts
• Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
• Demonstrated strength in data modeling, ETL development, and data warehousing
• Experience using business intelligence reporting tools (Quicksight, Tableau etc.)
• Proven success in communicating with users, other technical teams, and senior management to collect requirements, describe data modeling decisions and data engineering strategy
• Experience providing technical leadership and mentoring other engineers for best practices on data engineering
• Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
• Mindset and analytical skills to towards continuous improvement and have an edge to always research on latest technologies