Lead Data Engineer
Multiple positions available, P-IM002
Posted on 1/18/2024
INACTIVE
84.51 Degrees

1,001-5,000 employees

Retail data science company enhancing customer experiences
Company Overview
84.51° stands out as a leading retail data science company, utilizing advanced data science and predictive analytics to create personalized experiences for shoppers, thereby providing a competitive edge to its clients. The company's unique strength lies in its access to first-party retail data from nearly half of US households and over 2 billion transactions, which enables a more customer-centric approach. With its specialized services like 84.51° Insights, 84.51° Loyalty Marketing, and Kroger Precision Marketing, 84.51° helps brands effectively engage customers at every point of their purchasing journey.
Consumer Goods
Data & Analytics

Company Stage

N/A

Total Funding

$5.5M

Founded

2015

Headquarters

Cincinnati, Ohio

Growth & Insights
Headcount

6 month growth

2%

1 year growth

8%

2 year growth

24%
Locations
Chicago, IL, USA • Highland Park, IL, USA • Cincinnati, OH, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Agile
Python
Git
Apache Spark
SQL
Java
Apache Hive
Hadoop
CategoriesNew
Data & Analytics
Requirements
  • Bachelor’s degree in Computer Science or a closely related technical field
  • 5 years of professional IT experience or 3 years with a Master’s degree
  • 2 years of experience in designing and developing software using Big Data tools such as Hadoop, Spark, Pyspark, or Hive
  • Experience in Agile principles in scrum teams
  • Relational data modeling experience
  • Experience with SQL, Hadoop/HDFS, data warehousing, and ETL concepts
  • Version Control Software experience such as GitHub
  • Programming language experience with Java, Scala, or Python
Responsibilities
  • Develop strategies and solutions to ingest, store and distribute big data
  • Lead the design and development of big data solutions including Hadoop and SQL based solutions
  • Perform unit and integration testing
  • Collaborate with senior resources to ensure consistent development practices
  • Provide mentoring to junior resources
  • Participate in retrospective big data software reviews
  • Participate in the estimation process for new work and releases
  • Bring new perspectives to big data engineering problems