Entry Level Data Engineer

Experience Level - 0 to 2 Years
Even Freshers can apply for this Job Role. (OPT Candidates also can apply)
Kindly Fill the Google Form, by clicking the below Link - ONLY CANDIDATES WHO HAVE FILLED THE GOOGLE FORM WILL BE CONTACTED

https://docs.google.com/forms/d/e/1FAIpQLSf9yLY4sKvZrpPd6wwSeS-gcBTNmzVq601bZlcqANcwot4Vrg/viewform?usp=sf_link

As a Data Engineer , you will be responsible for designing, developing, and maintaining robust data pipelines and infrastructure. You will work closely with cross-functional teams to gather requirements, optimize data flow, and ensure data availability, reliability, and accuracy. Your expertise in Big Data tools, Scala, Spark, and related technologies will be pivotal in shaping our data architecture and driving actionable insights from our vast datasets.
Required Skills

• Bachelor's degree in Computer Science, Engineering, or a related field. Master's degree is a plus.
• Minimum of 0 to 2 years of proven experience as a Data Engineer, with a strong focus on Big Data technologies.
• Proficiency in Scala programming language.
• Hands-on experience with Apache Spark for large-scale data processing and analytics.
• In-depth knowledge of ETL processes and data integration techniques.
• Familiarity with distributed data storage and processing systems such as Hadoop, Hive, and HDFS.
• Experience with data modeling, schema design, and data warehousing concepts.
• Strong SQL skills and understanding of database systems (e.g., MySQL, PostgreSQL).
• Experience with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) processes.
• Excellent problem-solving skills and a proactive attitude towards troubleshooting and issue resolution.
• Ability to work effectively in a collaborative team environment and communicate technical concepts to non-technical stakeholders.
• Knowledge of cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) is a plus.
• Strong attention to detail and a passion for delivering high-quality, reliable data solutions.

Responsibilities

• Collaborate with data scientists, analysts, and stakeholders to understand data requirements and translate them into scalable data solutions.
• Design, develop, and deploy data pipelines for efficient data extraction, transformation, and loading (ETL) processes.
• Optimize and maintain existing data pipelines to ensure data quality, reliability, and performance.
• Implement data partitioning, clustering, and indexing strategies to enhance query performance.
• Monitor and troubleshoot data pipeline issues, ensuring timely resolution to minimize downtime and data loss.
• Work with large-scale datasets in a distributed computing environment using tools such as Hadoop, Spark, and related technologies.
• Explore and evaluate new technologies and techniques to improve data processing efficiency and effectiveness.
• Collaborate with DevOps teams to automate deployment processes and ensure a seamless integration of data pipelines.
• Ensure compliance with data security and privacy standards throughout the data lifecycle

Job Types: Full-time, Permanent

Pay: $50,021.25 - $60,240.65 per year

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Life insurance
• Paid time off

Experience level:

• 1 year
• 2 years
• 3 years
• No experience needed
• Under 1 year

Schedule:

• 8 hour shift

Work Location: Remote