Senior Big Data Developer
4 weeks ago
We are seeking a highly skilled Senior Big Data Developer to lead our engineering team in designing, developing, and maintaining large-scale data processing systems. The successful candidate will work closely with cross-functional teams to architect scalable data pipelines and provide valuable insights through advanced data processing and analytics.
Key Responsibilities:
- Data Pipeline Development: Design, develop, and optimize large-scale ETL (Extract, Transform, Load) processes and data pipelines.
- Big Data Management: Work with large datasets, implementing solutions to capture, process, and analyze data from various sources.
- Data Warehousing: Build and maintain scalable, reliable, and efficient data warehouses.
- Cloud & Distributed Systems: Implement data solutions using distributed computing tools such as Apache Hadoop, Apache Spark, and cloud platforms like AWS, Azure, or GCP.
- Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand data needs and translate them into technical requirements.
- Performance Tuning: Ensure performance, reliability, scalability, and security of big data platforms.
- Mentoring & Leadership: Mentor junior team members and contribute to the overall success of the team by driving technical excellence.
Requirements:
- Technical Expertise: 7+ years of experience in big data development, including a deep understanding of data pipelines, architectures, and distributed systems.
- Proficiency: Proficiency with big data tools such as Apache Hadoop, Apache Spark, Kafka, Hive, HBase, and others.
- Programming Skills: Strong experience with Python, Scala, Java, or SQL.
- Cloud Platforms: Expertise with cloud platforms like AWS, Azure, or GCP for big data processing (experience with services like EMR, S3, Redshift, Databricks, etc.).
- Data Modeling: Solid understanding of data modeling, ETL development, and data warehousing principles.
- NoSQL Databases: Hands-on experience with NoSQL databases like Cassandra, MongoDB, or Couchbase.
- CI/CD Pipelines: Experience with CI/CD pipelines and familiarity with containerization tools (e.g., Docker, Kubernetes) is a plus.
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