Overview
Intuit is a leading software provider of business and financial management solutions for small and mid-sized businesses, consumers and accounting professionals.
You probably know us by our flagship products, QuickBooks®, TurboTax® and Mint®, but that is just the start.
We are currently going through a fundamental transformation to a global financial solutions and services company.
Come join Intuit as part of the DSE Risk Data Analytics team as a Senior Data Engineer.
We are looking for creative problem solvers with a passion for innovation to join our team and revolutionize the way the world does business.
This is a full-time position, preferably in our Woodland Hills, CA location.
This position may also require some occasional travel to our corporate office in Mountain View, CA.
The primary role of a Senior Data Engineer is to provide timely technical, analytical and data support to the policy analyst and data science community to define, test, implement, and maintain strategies and procedures that will assist Intuit in growing revenue and profit by developing and maintaining innovative products and techniques in the target markets we serve.
What you’ll bring
BS in Computer Science, Mathematics, or a similar
Object Oriented programming skills in Python, and a willingness to learn other languages (e.g.
Bash, Scala, R) as
Experience developing data processing applications using
Strong understanding of HADOOP-based technologies and systems, including Hive QL, MR, and Spark programming, Spark cluster/job optimization
Familiarity with database fundamentals including SQL, and schema
4+ years of experience integrating technical processes and business outcomes specifically: data architecture and models, data and process analysis, data quality metrics / monitoring, developing policies / standards & supporting
4+ years of hands-on data engineering
2+ years DevOps experience including configuration, monitoring and version
Record of accomplishment working with data from multiple sources, willingness to dig-in and understand the data and to leverage creative thinking and problem
Excellent interpersonal and communication skills, including business writing and presentations.
Ability to communicate objectives, plans, status and results clearly, focusing on critical few key
Preferred Qualifications
MS in Computer Science, Mathematics, or a similar
2+ years of experience building and operating scalable and reliable data pipelines based on Big Data processing technologies like Hadoop, MR, Spark in the AWS
Advanced programming skills in Python, Bash Familiarity with Scala and R.
5+ years of experience integrating technical processes and business outcomes specifically: data architecture and models, data and process analysis, data quality metrics / monitoring, developing policies / standards & supporting
5+ years of hands-on data engineering
2+ years of hands-on experience with analytics, building and operating DW on Redshift or Vertica
Demonstrated ability to work in a matrix environment, ability to influence at multiple levels, and build strong
Knowledge of enacting service level agreements and the appropriate escalation and communication plans to maintain
How you will lead
Work in the Risk Data Analytics data engineering team.
The team has 10 engineers working on Risk & Compliance Management, Fraud Prevention, EMR/Spark/Python data pipelines, Data Warehousing (DW), and Business Intelligence (BI)
Work closely with the Risk Decision Science team to design, build, deploy and operate their data science, data analytics
Work in fast-moving development team using agile
Partner closely with Data Scientists, BI developers and Product Managers to design and implement data models, database schemas, data structures, and processing logic to support various data science, analytics, machine learning, and BI
Design and develop ETL (extract-transform-load) processes to validate and transform data, calculate metrics and model features, populate data models etc., using Spark, Python, SQL, and other technologies in the AWS
Lead by example, demonstrating best practices for code development and optimization, unit testing, CI/CD, performance testing, capacity planning, documentation, monitoring, alerting, and incident response in order to ensure data availability, data quality, usability and required
Define SLAs for data availability and correctness.
Automate data availability and quality monitoring and Respond to alerts when data delivery SLAs are not being met.
Communicate progress across organizations and levels from individual contributor to executive.
Identify and clarify the critical few issues that need action and drive appropriate decisions and actions.
Communicate results clearly and in actionable