The Rapid and Rural Logistics (R2L) org is seeking an exemplary Data Engineer with broad technical skills to develop, own and support pipelines that back business analytics, to build tools that facilitate orchestration, and to produce automation scripts that optimize infrastructure. We look for candidates who are excellent communicators, self-motivated, flexible, hardworking, and who like to have fun.

This role is on a large analytical team that supports a wide range of businesses including sub same day, Rural super rural etc. This role has great exposure to a broad scope that can really help shape the future of operational fulfillment and promotes career progression.

We are looking for someone with preferred experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose and Lambda. Experience working independently on and completing end-to-end projects. Knowledge of professional software engineering and best practices for the full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence

Key job responsibilities
Main responsibilities of this role include but are not limited to:

- Manage and grow a database infrastructure
- Develop automation solutions through programming languages
- Support analytical researches and provide recommendations to business challenges
- Use best practices for data modeling, ETL/ELT procedures, SQL, Redshift, and OLAP technologies to implement data structures.
- Experience in at least one modern scripting or programming language, such as Python, Typescript, or Scala.
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Collect and convert functional and business requirements into solutions that are operable, scalable, and well-suited to the overall data
- Determine best practices for creating data lineage from a range of data sources by analyzing source data systems. data sources.
- Engage in all phases of the development life cycle, including design, implementation, testing, delivery, documentation, support, and maintenance.
- Generate complete, reusable metadata and dataset documentation.