Python Data Engineer
3 weeks ago
We are seeking a talented Data Engineer experience in Quantexa with expertise in Hadoop, Scala, Spark, Elastic, Open Shift Container Platform (OCP) and DevOps practices.
As a Data Engineer, you will play a crucial role in designing, developing, and optimizing big data solutions using Apache Spark, Scala, and Elasticsearch.
You will collaborate with cross-functional teams to build scalable and efficient data processing pipelines and search applications.
Knowledge and experience in the Compliance / AML domain will be a plus.
Working experience with Quantexa tool is a must.
Requirements
- Bachelor's or master’s degree in computer science, Software Engineering, or a related discipline
- Possession of Quantexa certification as a Data Engineer or Data Architect, with proficiency in the tool
- Proficiency in programming languages commonly employed in data engineering, including Spark, Python, Scala
- Demonstrated experience as a Data Engineer, utilizing Hadoop, Spark, and data processing technologies in large-scale environments
- Expertise in the Scala programming language and familiarity with functional programming principles and Anomaly Detection.
- Prior experience with the Quantexa tool is highly desirable
- Comprehensive understanding of Apache Spark architecture, including RDDs, DataFrames, and Spark SQL
- Advanced proficiency in designing and developing data infrastructure utilizing Hadoop, Spark, and associated tools (HDFS, Hive, Pig, etc.)
- Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration via Kubernetes
- Knowledge of DevOps methodologies, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
- Experience with Graphana, Prometheus, and Splunk will be considered an added advantage
- Background in integrating and utilizing Elasticsearch for data indexing and search applications
- Solid understanding of Elasticsearch data modeling, indexing strategies, and query optimization techniques
- Experience with distributed computing, parallel processing, and handling large datasets
- Proficient in performance tuning and optimization methods for Spark applications and Elasticsearch queries
- Strong problem-solving and analytical capabilities with the capacity to debug and resolve intricate issues
- Familiarity with version control systems (e.g., Git) and collaborative development environments
Tell employers what skills you have
Apache Spark
Scala
Kubernetes
Big Data
Data Modeling
Pipelines
Architect
Hadoop
Software Engineering
Data Engineering
SQL
Python
Performance Tuning
Docker
Orchestration
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