Architecting scalable data pipelines for efficient data ingestion, processing, and storage.

Developing and optimizing big data processing jobs using Spark, Hadoop, and related technologies.

Designing and maintaining data warehouses for business intelligence and reporting purposes.

Managing and optimizing large-scale databases, including NoSQL and relational databases.

Analyzing large datasets to identify trends, patterns, and insights for business decision-making.

Building and maintaining ETL (Extract, Transform, Load) processes for data integration.

Integrating big data solutions with cloud platforms like AWS, Azure, and GCP.

Optimizing the performance of big data systems and applications.

Implementing data security measures to protect sensitive information.

Setting up monitoring and alerting systems for big data infrastructure.

Collaborating with data scientists, analysts, and other stakeholders.

Creating and maintaining documentation for big data systems and processes.

Design and develop scalable big data solutions using Hadoop, Spark, and other distributed computing frameworks.

Build data pipelines for extracting, transforming, and loading (ETL) large datasets from various sources.

Implement data governance policies and ensure data quality, consistency, and security across the big data platform.

Develop and maintain data models, schemas, and metadata repositories for efficient data storage and retrieval.

Optimize big data infrastructure for performance, scalability, and cost-effectiveness.

Collaborate with data scientists and analysts to understand their data requirements and provide them with the necessary data access and tools.

Monitor and troubleshoot big data systems, identify performance bottlenecks, and implement solutions to improve system reliability.

Automate data processing tasks and workflows using scripting languages such as Python or Scala.

Stay up-to-date with the latest big data technologies and trends, and evaluate new tools and frameworks for potential adoption.

Implement data security measures, including access controls, encryption, and data masking, to protect sensitive data.

Develop and maintain documentation for big data systems, including architecture diagrams, data flow diagrams, and operational procedures.

Participate in code reviews and contribute to the development of best practices for big data engineering.

Work with cloud platforms like AWS, Azure, or GCP to deploy and manage big data solutions in the cloud.

Design and implement real-time data streaming solutions using technologies like Kafka or Apache Flink.

Ensure compliance with data privacy regulations, such as GDPR or CCPA, when handling personal data.