
Senior Data Engineer
3 weeks ago
The incumbent will be responsible for designing, building, and optimizing scalable data analytics systems and pipelines, impacting the efficiency, reliability, and performance of data-driven solutions to enable high-quality insights and informed decision-making across the organization.
RESPONSIBILITIES- Design and implement scalable data ingestion and processing workflows using Apache NiFi and Apache Spark.
- Optimize and maintain analytical databases such as Vertica and ClickHouse for high-performance querying on large datasets.
- Administer and tune MySQL Server and MongoDB to ensure reliability, availability, and optimal performance.
- Develop robust data models for both OLTP and OLAP systems to support analytics and operational use cases.
- Build and manage distributed data architectures integrated with microservices, ensuring seamless and secure data access.
- Apply advanced query optimization and data lifecycle management practices to enhance overall system efficiency.
- Managing the complexity of designing and maintaining scalable data pipelines and distributed systems that can reliably process and serve 100 M+ row datasets across diverse platforms.
- Ensuring optimal performance and availability of analytical databases (e.g., Vertica, ClickHouse) while balancing cost, speed, and storage efficiency in high-demand environments.
- Integrating data workflows within microservice-based architectures, while maintaining data consistency, security, and low-latency access across teams and systems.
Required Qualification
- Bachelor's degree in Computer Science, Data Science, Data Engineering, Software Engineering, or other related fields.
Required Experience
- At least 5 years of professional experience in data engineering or software development with focus on large scale data solutions.
Required Skills
- Hands-on experience with Apache NiFi or Apache Spark for data ingestion and processing.
- Strong experience in MySQL Server and MongoDB database administration and optimization.
- In-depth understanding of Linux-based environments and shell scripting.
- Strong knowledge of data modeling principles for both OLTP and OLAP systems.
- Experience in building and optimizing distributed datastores for high availability and performance.
- Familiarity with microservice architectures and their integration with data platforms.
- Expertise in query optimization techniques to enhance system performance.
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