Data Engineer

Job Title: Data Engineer (2 Positions)

Experience: 6–7 Years
Location: Remote / Hybrid
Employment Type: Full-Time

Job Summary

We are seeking talented and experienced Data Engineers with 6–7 years of hands-on experience in designing, developing, and maintaining large-scale data solutions. The ideal candidates will have a strong background in building robust ETL frameworks, complex data pipelines, and working with big data technologies. This role requires a blend of technical expertise, problem-solving skills, and the ability to work collaboratively with cross-functional teams to deliver reliable and scalable data systems supporting analytics and business intelligence.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT frameworks and complex data pipelines.

• Build and optimize data processing jobs using big data technologies and modern data frameworks.

• Ensure data accuracy, consistency, and reliability across multiple data platforms.

• Collaborate with data scientists, analysts, and business stakeholders to transform business requirements into data-driven technical solutions.

• Troubleshoot, debug, and optimize existing workflows for performance and scalability.

• Implement best practices for data governance, version control, and performance monitoring.

Required Skills & Qualifications

• 6–7 years of hands-on experience in data engineering and ETL design.

• Proficiency in SQL and strong experience with one or more ETL tools such as Talend, Informatica, or dbt.

• Expertise in big data technologies including Apache Spark, Hadoop, and Kafka.

• Solid programming skills in Python or Scala.

• Strong understanding of data modeling, data integration, and data quality management.

• Experience with cloud data platforms (AWS, Azure, or GCP) is a strong plus.

• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications

• Experience working in agile data environments.

• Exposure to data warehousing or data lake architectures.

• Familiarity with DevOps practices and CI/CD for data pipelines.

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