Data Engineer
1 month ago
Job Overview :
We are seeking a highly skilled and motivated Scala Data Engineer to join our team. In this role, you will design, build, and maintain large-scale data processing systems. The ideal candidate will have a strong background in Scala programming, distributed data processing frameworks, and a deep understanding of data architecture and pipelines. You will collaborate closely with data scientists, data analysts, and other engineering teams to optimize the data infrastructure.
Key Responsibilities :
- Data Pipeline Development : Design, develop, and maintain efficient, reliable, and scalable data pipelines using Scala and distributed frameworks like Apache Spark or Kafka.
- Data Integration : Integrate data from various sources into data lakes or data warehouses, ensuring high data quality and performance.
- Performance Optimization : Optimize existing processes and code for performance, scalability, and reliability, especially in distributed environments.
- Data Architecture : Help define and implement data architecture that supports business analytics, machine learning, and reporting needs.
- Collaboration : Work closely with data scientists, analysts, and other engineers to translate data requirements into technical solutions.
- Monitoring & Maintenance : Build tools and processes for monitoring, alerting, and ensuring the reliability of the data infrastructure.
- Testing & Debugging : Write unit, integration, and end-to-end tests for data pipelines and ensure data integrity across systems.
- Documentation : Provide clear, concise documentation for both technical and non-technical stakeholders.
Requirements :
- Experience : 7+ years of experience in data engineering, with a focus on Scala and big data technologies.
- Programming Languages : Proficient in Scala; experience with Java, Python, or other JVM-based languages is a plus.
- Big Data Frameworks : Hands-on experience with Apache Spark, Kafka, Hadoop, or similar frameworks.
- Data Storage Systems : Experience with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., Cassandra, HBase).
- Data Warehousing : Familiarity with data warehousing solutions like Amazon Redshift, Google BigQuery, or similar.
- Cloud Platforms : Experience with cloud-based data platforms (AWS, Azure, GCP).
- Data Processing : Strong understanding of distributed data processing, batch, and streaming data processing techniques.
- Version Control : Proficiency in using version control systems like Git.
- Problem-Solving Skills : Strong analytical and problem-solving skills with a focus on continuous improvement.
- Communication : Excellent communication and teamwork skills, with the ability to translate technical issues into business context.
Preferred Qualifications :
- Machine Learning : Experience with data pipelines supporting machine learning models.
- ETL Tools : Familiarity with ETL tools like Apache NiFi, Talend, or Airflow.
- CI/CD : Experience with continuous integration and deployment pipelines for data pipelines.
- Microservices : Experience building and managing microservices for data-intensive applications.
- DevOps : Familiarity with DevOps practices, especially as they apply to data engineering.
(ref:hirist.tech)
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