Data Engineer

We are hiring for below position kindly send your CV at hr@Scaleablesolutions.com
Position: Data Engineer
Location: Remote (Work from Home)
Key Responsibilities
Day-to-day responsibilities include:
 Workshop and contribute to the design of new data processing systems and pipelines
 Support the delivery of data science projects (feature generation, ML model delivery)
 Elicit business requirements with the assistance of our Business Analyst
 Translate acceptance criteria to tests and working code
 Review and submit feedback to your teammates’ pull requests
 Contribute to the improvement of development processes and pipeline
 Monitor data processes and respond to alerts
Knowledge, Skills & Experience
Experience:
 2-3 years of experience in a developer role
 Previous experience developing data pipelines and data-driven software applications
 Previous experience in SQL development and database administration
 Proven ability to drive complex design and development efforts in an agile environment
Software Engineering:
 Strong in fundamental programming techniques and concepts (OOP Patterns, TDD, functional programming concepts, algorithms and data structures, distributed computing)
 A polyglot developer – very strong skills in at least two of the following: Scala, Python, Go, PHP with Java, Kotlin and C/C++ being advantageous
 Experience in developing and managing systems using cloud computing products (ideally AWS)
Data Engineering:
 General Data Architecture knowledge such as Data Lakes, Data Warehouses and Data Marts, as well as SQL, NoSQL and Graph databases
 Solid experience in at least two RDBMS’s, and demonstrable SQL capability including query optimisation and tuning
 An understanding of the use cases, and choices available, in the following technological groups:
 Data Processing (e.g. Spark, Beam)
 Data Workflows (e.g. Airflow, Luigi)
 Data Formats (e.g. Avro, Parquet)
 Messaging Systems (e.g. Kafka, RabbitMQ, NATS)
Favourable Skills and Experience:
 Docker and related container-orchestration technologies (e.g. Kubernetes)
 Trading fundamentals and fintech systems (e.g. FIX protocol)
 Machine Learning & Data Science fundamentals and technologies (such as Torch, TensorFlow, scikit-learn, pandas, and Jupyter)



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