Data & Platform Engineering

Synopsis
The ideal candidate is an experienced data pipeline builder and data wrangler who utilizes the latest technology to implement and support platforms for analytical use-cases. The selected candidate will support our software developers, database architects, data analysts, and data scientists on data initiatives and will ensure that the data delivery architecture remains consistent across ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products. The right candidate will be excited by the prospect of optimizing or re-designing our company's data architecture to support our next generation of products and data initiatives. They will also be responsible for monitoring analytical systems to ensure smooth operation.
Accountabilities

1. Evaluate business needs and objectives
2. Identify opportunities for data acquisition
3. Analyze and organize raw data
4. Create and maintain optimal data pipeline architecture, interpret trends and patterns, build algorithms and prototypes
5. Assemble large, complex data sets that meet functional and non-functional business requirements
6. Identify, design, and implement internal process improvements such as automating manual processes, optimizing data delivery, and redesigning infrastructure for scalability
7. Build infrastructure for optimal extraction, transformation, and loading of data from various sources using SQL and big data technologies
8. Develop analytics tools that utilize data pipelines to provide actionable insights into customer acquisition, operational efficiency, and other key metrics

1. 3-8 years of experience in a Data Engineer role, with a Graduate degree in Computer Science, Statistics, Informatics, Information Systems, or a related quantitative field
2. Technical expertise in data models, data mining, and segmentation techniques
3. Strong analytical skills for working with unstructured datasets
4. Experience building and optimizing data pipelines, architectures, and datasets
5. Advanced SQL knowledge and experience with relational databases, query writing, and familiarity with various databases
6. Experience performing root cause analysis to answer business questions and identify improvement opportunities
7. Ability to troubleshoot and solve complex problems
8. Basic understanding of UNIX batch/shell scripting and SQL databases
9. Experience supporting data transformation, data structures, metadata, dependency, and workload management processes