Position: Data Engineer
Company: GlobalLogic
Experience required: 3y
English level: upper

Client company establishes a root of trust, and then grants least privilege access just-in-time based on verifying who is requesting access, the context of the request, and the risk of the access environment. 
Client's products centralizes and orchestrates fragmented identities, improves audit and compliance visibility, and reduces risk, complexity, and costs for the modern, hybrid enterprise. Over half of the Fortune 100, the world’s largest financial institutions, intelligence agencies, and critical infrastructure companies, all trust this company to stop the leading cause of breaches – privileged credential abuse.

Job Description
PhD, Master or Bachelor degree in Computer Science, Math, Physics, Engineering, Statistics or another technical field;
Experience in IT for more than 4 years;
Experience in Data Engineering for more than 4 years;
Good understanding of major programming languages like Java, Scala, Python;
Familiar with defining Data Governance concepts (incl. data lineage, data dictionary);
Strong knowledge in data modelling, query optimization on different storage solutions such as RDMS, document stores;
Commercial experience with:
building and optimizing big data pipelines, architectures and data sets;
performing root cause analysis on internal and external data processes;
Distributed systems, Big Data technologies, Streaming technologies (e.g. Hadoop, Spark, Kafka, Data Lakes);
variety of database and data warehouse technologies (including SQL Server, PostgreSQL, RedShift and Cassandra);
CI/CD;
Experience with different delivery and development processes and practices:
Troubleshooting, profiling and debugging;
Agile, SCRUM software processes and technologies;
Code Review process;
Refactoring process;
Upper-Intermediate English.

Desirable
Certifications on:
Cloud Computing Platforms (e.g. AWS, GCP, Azure);
Commercial experience with:
Cloud Computing Platforms (e.g. AWS, GCP, OpenShift);
Orchestration and containerization (Kubernetes, Dockers); 
Graph databases, time-series databases, data warehouses;
Creation of software architecture and design of complex solutions;

Job Responsibilities
Develop and maintain optimal data pipeline architecture;
Identify ways to improve data reliability, efficiency and quality;
Prepare data for predictive and prescriptive modeling Build predictive models and machine-learning algorithms;
Create data tools for data scientist team members; 
Taking part in the decision-making process in design, solution development and code review;
Working in an international distributed team;
Communicate with PM, PO, Developers and other colleagues and stakeholders;
Delivering the product roadmap and plannings, create estimations;