Data Architecture & Design: Design and implement scalable, high-performance data systems and pipelines to support large datasets, data processing, and analytical workflows.
Data Processing: Develop and manage ETL (Extract, Transform, Load) pipelines for data ingestion, processing, and transformation. Utilize big data technologies such as Apache Hadoop, Spark, Kafka, and other related frameworks.
Data Storage: Implement and manage data storage solutions using both relational and NoSQL databases (e.g., HDFS, Hive, Cassandra, MongoDB, etc.).
Data Integration: Integrate data from various structured and unstructured sources, ensuring that data is accurately transformed and processed.
Optimization & Performance Tuning: Monitor, optimize, and troubleshoot the performance of big data pipelines and systems to ensure efficient and reliable data processing.
Collaboration: Collaborate with data scientists, analysts, and other team members to understand data requirements and provide data-driven solutions for business needs.
Data Security: Ensure proper security measures are in place to protect sensitive data in compliance with company policies and industry regulations.
Documentation: Document data pipelines, architectures, and workflows for team reference and future scalability.