Senior Data Engineer
7 months ago
We are looking for Senior Data engineers to join our fast-growing analytics and AI team, you will be responsible for designing and optimizing data pipeline architecture as well as optimizing data flow and collection for cross functional teams
- Technologies
- Python (fastapi, cython, scipy, Scikit-learn, Seaborn )
- Spark(Pyspark or Scala)
- Big data Tools
- Databases (SQL,NoSQL) Data warehouse
- Responsibilities
- Develops and maintains scalable data pipelines and builds out new API integrations to support continuing increases in data sources, volume and complexity.
- Data manipulation and cleansing
- Operate and optimize AI solutions
- Act as focal point between AI Software Engineering and Data sources
- Data Modelling and Platforming for analytics
- Develop, construct, test and maintain architectures
- Align architecture with business requirements
- 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 Remaining up-to-date with industry standards and technological advancements that will improve the quality of your outputs.
- Job Requirements
- BSc or Advanced degree in Data Science, Data Engineering, Statistics, Computer Science, or similar.
- Master Degree in a related field is a plus
- At least 3-5 years of experience in a similar position or in a Data engineering Role
- Experience With a Big Data & Hadoop Ecosystem, with experience in data profiling and transformations
- Experience with PySpark core, or PySpark SQL, and PySpark Dataframe
- Experience with core Java (and/or Scala, Python)
- Experience with NoSQL databases such as HBase, Cassandra, or MongoDB
- Experience with SQL databases
- Proficient in designing efficient and robust ETL/ELT workflows, schedulers, and event-based triggers
- Strong critical thinking, decision making, and problem-solving skills
- Excellent verbal/written communication skills, including communicating technical issues to non-technical audiences
- Exposure to Spark Streaming and Machine Learning
- Exposure to Data Mining, Data Engineering, and Data Modeling
- Exposure to Graph Databases
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