Senior Data Engineer
4 days ago
**Long Description**:
Data Engineer role is to use their Data knowledge to build, optimize and maintain data sets, data pipeline and architecture. Responsible about maintain and develop data processing software and tools.
**Technical Qualifications**:
- Experience with SQL / Python / Data Structures and Algorithms.
- Good Knowledge in Cloud Platforms Preferable GCP and/or AWS.
- Good knowledge in Big Data and Data Science technologies
- Good knowledge in data catalogue tools (e.g., Collibra).
- Familiar with data movement techniques and best practices to handle large volumes of data.
- Experience with data warehousing architecture and data modelling best practices.
- Hands-on experience in data modelling, data visualization, and pipeline design & development.
- familiar with DevOps tool stack (e.g. Jenkins, Ansible, github, Chef)
- Good knowledge in UNIX/Linux administration.
- Good knowledge in Apache Spark, Apache Beam.
**Personal Qualifications**:
- Bachelor Degree in computer science and/or engineering or equivalent with 0-3 years of relevant experience
- Working in international, distributed teams
- Should have good analytical & troubleshooting skills
- Should have good verbal and written communication skills
- System analysis skills for IT troubleshooting
- Conscientiousness and high accuracy in work
- Understands business requirements
- Flexible attitude and can do attitude
- familiar with cognitive technologies
- Hands on experience in working within agile development environment preferably as part of multi-locations and/or virtual teams
- Strong communication skills
- Self-motivated and creative problem-solving skills
- Innovative and out of box thinking
- Fully comfortable working in English, both in writing and spoken
**Roles & Responsibilities**:
- Develop, construct, test and maintain architectures.
- Align architecture with business requirements via developing/configure new business requirements.
- Develop data set processes.
- Use programming language and tools.
- Identify ways to improve data reliability, efficiency, and quality.
- Deploy sophisticated analytics programs, machine learning and statistical methods.
- Prepare data for predictive and prescriptive modeling.
- Find hidden patterns using data.
- Use data to discover tasks that can be automated
_VOIS #movewithus
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