Data Engineer II
5 days ago
Responsibilities :
- Data Pipeline Development : Design, develop, and maintain scalable, robust, and reliable data pipelines on Google Cloud Platform (GCP).
- Big Data Processing : Implement and optimize large-scale data processing workflows using Apache Spark, Apache Beam, & Apache Flink for both real-time and batch data processing.
- Programming & Automation : Develop efficient and maintainable code using Scala, Java, and Python for a variety of data engineering tasks. Automate data workflows to ensure seamless and fault-tolerant operations.
- Cross-functional Collaboration : Partner with data scientists, analysts, and business teams to understand data needs, design data solutions, and ensure data is readily available for insights and decision-making.
- Data Quality & Governance : Ensure data quality, governance, and security standards across the data pipelines, ensuring the integrity and privacy of our data.
- Optimization & Performance : Continuously monitor and optimize data pipelines for performance and scalability, reducing latency and improving the throughput of the systems.
Requirements :
- Educational Background : Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related fields.
- Experience : 3-5 years of hands-on experience in data engineering with a focus on building scalable and efficient data architectures.
- Proficiency with Scala, Java, and Python for data processing, scripting, and automation.
- Advanced skills in Scala and Java for developing high-performance data solutions.
- Strong experience with Google Cloud Platform (GCP) services such as BigQuery, Dataflow, and DataProc Pub/Sub.
- Expertise in SQL for querying and optimizing large datasets.
- Big Data and Distributed Systems : Strong understanding of big data technologies such as Apache Spark, Kafka, and NoSQL databases.
- Data Modeling and Warehousing : Experience with data modeling, warehousing, and OLAP/OLTP systems.
- Cloud Experience : Hands-on experience with other cloud platforms (AWS, Azure) is a plus.
- Collaboration and Communication : Ability to work in a collaborative team environment and communicate technical concepts clearly to non-technical stakeholders.
- Experience with real-time data streaming technologies such as Kafka, Flink, or Kinesis.
- Experience with machine learning pipelines or AI-driven data workflows is a plus.
- Familiarity with DevOps tools and practices such as Docker, Kubernetes, and CI/CD pipelines in a data engineering context.
- Knowledge of Business Intelligence (BI) tools like Tableau, PowerBI, or Looker.
(ref:hirist.tech)-
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