Data Engineer

NinjaHoldings•Chicago, IL

13h•Hybrid

About The Position

NinjaHoldings was founded in 2017 by a team seeking to revolutionize the way everyday Americans interact with financial services. Through our CreditNinja and NinjaCard brands, we empower people overlooked by traditional financial institutions to take control of their finances via a full suite of digital banking and lending products, providing incentives and rewards along the way as we guide them on a path to financial improvement. Through our NinjaEdge brand, we help companies better understand their customers by offering a package of bespoke underwriting, fraud detection, and analytics services. With offices in Chicago, Miami, and around the world through the power of remote work, we are a lean and innovative team always seeking like-minded talent to join us in our fight to disrupt consumer finance. Job Summary We are looking for a Data Engineer to join a team of analytics and machine learning experts. The hire will be responsible for building tooling to support analytics, helping to extend our machine learning platform, expanding and optimizing our data pipeline architecture, supervising junior engineers, and interfacing with the Development team to create cross-team solutions. The ideal candidate is an experienced data engineer and data wrangler who enjoys optimizing data systems and building them from the ground up. They must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products. Experience in analytics and statistics is a major bonus. The right candidate will be excited by the prospect of optimizing or even re-designing, our company’s data architecture to support our next generation of products and data initiatives; as well as mentoring and guiding junior members of the team.

Requirements

3+ years of experience in a Data Engineer role
Bachelors degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field
Advanced working SQL knowledge and experience working with relational databases (including Postgres and MySQL), query authoring (SQL), as well as working familiarity with a variety of databases
Experience building data pipelines, architectures, and data sets from raw, loosely structured data
A history of focusing on test driven design and results for repeatable and maintainable processes and tools
Experience building processes supporting data transformation, data structures, metadata, dependency, and workload management
Working knowledge of message queuing, stream processing, and highly scalable data stores
Strong project management and organizational skills and the ability to work independently in a fast-paced, quickly changing environment.
Ability to keep up with several projects at once and understand the impact of projects within a larger system
Experience supporting and working with cross-functional teams in a dynamic environment
Experience managing junior engineers and guiding a team of engineers through project planning, execution, and quality control stages
Experience with object-oriented design in Python
Experience with data pipeline and workflow management tools
Experience with AWS cloud services: EC2, RDS, Redshift, Glue, S3

Nice To Haves

Experience in analytics and statistics is a major bonus.
Strong analytic skills and understanding statistical methodologies
Experience building machine learning models
Experience handling data from acquisition to usage in models
Experience building and maintaining RestAPI systems, Flask apps, and state machines
Experience with continuous integration, especially in a data science context
Experience with Ruby on Rails

Responsibilities

Supervise junior members of the data engineering team. Guiding, planning, and reviewing the team's work
Create and maintain optimal data pipeline architecture
Assemble large, complex data sets that meet functional / non-functional business requirements
Extend our machine learning platform by designing tools that interface with cloud services, our current code base, and provide new flexibility in model building
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, Python, and AWS
Build analytics tools to provide actionable insights into key business performance metrics, as well as supporting the needs of the analytics team
Create data-handling tools for analytics and data scientist team members that assist them in building and optimizing our decision-making process