Big Data Engineer

Overview

A Big Data Engineer is instrumental in designing, constructing, and managing the vast infrastructure required to process and analyze large volumes of data. They work on developing scalable, efficient systems that can handle the complexity and velocity of big data, leveraging technologies such as Hadoop, Spark, and NoSQL databases. Their responsibilities extend beyond creating these systems; they also involve ensuring data quality, integrating multiple data sources, and maintaining the overall integrity of the data ecosystem.

Roles and Responsibilities

• Designing Big Data Solutions: Architect and implement solutions capable of efficiently processing, storing, and analyzing large datasets. This involves selecting the right big data technologies and frameworks to meet the organization's specific needs.
• Building and Maintaining Data Pipelines: Develop robust data pipelines that can automate data flow from various sources into the system. These pipelines must ensure data is accurately collected and made available for timely analysis.
• Data Storage and Management: Implement and manage scalable, reliable, and secure data storage solutions. This includes databases, data lakes, and any other form of data storage that meets the organization's needs for big data.
• Ensuring Data Quality and Integrity: Develop processes and systems to monitor data quality, ensuring that the data used for analysis is accurate and consistent. This may involve cleaning data, detecting and correcting errors, and implementing data validation measures.
• Collaborating with Data Scientists and Analysts: Work closely with data scientists and analysts to provide them with the necessary data infrastructure and tools for advanced analytics. This involves understanding their data needs and ensuring they have access to clean, high-quality data.

Skills

• Knowledge of Hadoop, Spark, Kafka, and other big data processing frameworks is crucial for efficiently storing, processing, and analyzing large datasets.
• Strong programming skills, particularly in languages like Java, Scala, Python, and SQL, are essential for developing and managing big data applications and pipelines.
• Expertise in database technologies, including traditional SQL databases (like MySQL, PostgreSQL) and NoSQL databases (such as MongoDB, Cassandra), is important for data storage and management.
• Ability to design data models and understand data warehousing concepts to support the needs of BI and analytics applications.
• Experience with data pipeline and ETL (Extract, Transform, Load) tools, such as Apache NiFi, Talend, or Informatica, for moving and transforming data.
• Knowledge of machine learning algorithms and analytics tools can be beneficial for analyzing data and generating insights.
• Familiarity with cloud services (AWS, Google Cloud Platform, Azure) is advantageous, as many organizations leverage cloud storage and computing capabilities for big data.
• Understanding Linux environments and basic system administration can be critical for setting up and maintaining data processing environments.
• Knowledge of data security principles, compliance regulations, and governance practices to ensure data protection and management responsibly.

Contact Person: Rakesh HR

Contact Number: 9003745749

Experience: 0 - 4 Years

Location: Coimbatore

timings: 09.30 Am - 6.00 Pm

Job Types: Full-time, Permanent, Fresher

Pay: ₹472,749.16 - ₹1,822,266.88 per year

Benefits:

• Health insurance
• Provident Fund

Schedule:

• Day shift
• Monday to Friday

Supplemental Pay:

• Performance bonus
• Yearly bonus

Work Location: In person