Do you desire to work with large amounts of data? Are you interested in architecting data infrastructure while doing meaningful work? Have you always wanted your work to have a positive societal mission to help underserved communities? As a Data Engineer at Possible, you will work on interesting, high impact and large scale data engineering projects. Key responsibilities including; build and deliver automated data pipelines, ingest and transform data from a plethora of internal and external data sources to a data lake in the cloud, data security and integration with other systems at Possible. We are looking for a knowledgeable leader with an ability to lead and is passionate about data to solve business problems. This role requires a high level of collaboration and engineering calibre as you work cross-functionally to answer key strategic questions and drive architecture decisions that will help Possible achieve the growth ahead. This role reports directly to the Head of Data Science and ML.
Role & Responsibilities:
- Owning the design, and development of end to end data pipelines and workflows
- Engineering solutions to aggregate and automate large scale data flows from varying sources
- Collaborate with Engineers and Scientists in the organization to construct complex data sources for algorithms and machine learning models
- Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
- Build real time streaming from internal and external sources to provide insights to the business
- Build, analyze and present actionable data to Marketing/Credit Risk/Finance/Product teams
- Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers.
- Design our data models for optimal storage and retrieval and to meet critical product and business requirements
- Understand and influence logging to support our data flow, architecting logging best practices where needed
- Contribute to shared Data Engineering tooling & standards to improve the productivity and quality of output for Data Engineers across the company
- Improve data quality by using & improving internal tools to automatically detect issues
Work experience and education, knowledge and skills:
- 5+ years of experience as a Data Engineer or in a similar role
- Bachelor’s and/or Master’s degree, preferably in CS, or equivalent experience
- Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
- Experience with data modeling, data warehousing, and building ETL pipelines
- Knowledge of batch and streaming data architectures
- Experience with AWS technologies including Redshift, RDS, S3, EML or similar solutions built around Hive/Spark etc.
- Experience communicating with senior management as well as with colleagues from engineering, analytics, and business backgrounds
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Demonstrated strength in data modeling, ETL development, and data warehousing
- Knowledge of data management fundamentals and data storage principles
- Knowledge of distributed systems as it pertains to data storage and computing
- Proficiency in Python, SQL or other similar languages.