Taglynk | Principal Data Engineer | bangalore
2 months ago
Job Summary:
The Principal Data Engineer is responsible for leading the design, development, and optimization of scalable data pipelines, data lakes, and large-scale datasets. With over 9 years of experience in data engineering and expertise in tools like ElasticSearch, Redshift, and data lakes, this role ensures the efficient processing and storage of massive data streams, driving business intelligence and data-driven decision-making across the organization.
Key Responsibilities:
Data Architecture & Pipeline Development:
- Design and implement highly scalable, reliable, and efficient data pipelines to process large datasets using modern data engineering tools and practices.
- Architect and manage data lakes, enabling storage and querying of unstructured and structured data.
- Lead initiatives to optimize data retrieval and integration using ElasticSearch, Redshift, and other distributed systems.
Large Dataset Management:
- Oversee the storage, processing, and analysis of large datasets, ensuring data quality, security, and performance.
- Develop and maintain ETL processes to manage data flow across multiple sources, transforming and enriching data for analytics and reporting.
- Collaborate with data scientists and analysts to provide seamless access to data and drive analytical outcomes.
Data Governance & Compliance:
- Ensure data governance policies are adhered to, maintaining high standards of data quality, privacy, and security.
- Implement and enforce best practices for data access control, data lineage, and monitoring.
- Ensure compliance with industry standards and regulations regarding data protection (e.g., GDPR).
Performance Optimization:
- Continuously monitor and optimize the performance of ElasticSearch, Redshift, and other data infrastructure.
- Identify and resolve bottlenecks, and work on improving query performance and cost efficiency.
- Experiment with new technologies and tools to keep data engineering processes at the cutting edge.
Team Leadership & Mentorship:
- Lead and mentor a team of data engineers, providing guidance on best practices, coding standards, and architecture design.
- Collaborate with cross-functional teams (e.g., product, engineering, data science) to meet business requirements and drive data innovation.
- Participate in code reviews and ensure adherence to coding standards and architectural guidelines.
Innovation & Strategic Planning:
- Stay updated on emerging trends in big data technologies and proactively recommend improvements or changes to the existing data architecture.
- Contribute to the long-term strategy for data architecture, ensuring scalability and flexibility for future growth.
Key Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 9+ years of experience in data engineering, with at least 5 years of hands-on experience working with large datasets and managing data architectures involving ElasticSearch, Redshift, and data lakes.
- Proven experience in designing and implementing data pipelines, ETL processes, and distributed data systems.
- Expertise in cloud-based data platforms (AWS, Azure, etc.), particularly in managing Redshift and data lake infrastructure.
- Strong programming skills in Python, Java, or Scala, and experience with SQL.
- Solid understanding of data modeling, database design, and performance tuning for large-scale systems.
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