Data Engineering Manager

We are looking for a skilled Data Engineering Manager with 8 to 13 years of experience to lead our team in designing, developing, and optimizing scalable data pipelines and infrastructure. This position is based in Hyderabad.

• Lead a team of data engineers to design, develop, and optimize scalable data pipelines and infrastructure.
• Design, develop, and maintain scalable ETL (Extract, Transform, Load) pipelines for efficient data processing and integration.
• Build and maintain scalable, fault-tolerant data architectures ensuring efficient data storage, retrieval, and processing across cloud and on-premises environments.
• Oversee the design, implementation, and performance tuning of relational and NoSQL databases, ensuring data consistency, security, and accessibility.
• Automate data ingestion, transformation, and validation processes to enhance pipeline efficiency and reduce manual intervention.
• Develop cloud-based data solutions using platforms like AWS, Azure, or Google Cloud, leveraging technologies such as Spark, Hadoop, and Kafka for large-scale data processing.
• Collaborate with data scientists, analysts, and business intelligence teams to understand data requirements and develop scalable solutions for analytics and reporting.
• Proactively monitor data pipelines and systems for performance issues, troubleshoot failures, and implement preventive measures for system reliability.
• Partner with business and IT leaders to align data engineering initiatives with company goals, ensuring seamless integration of data-driven solutions across departments.

• Minimum 8 years of experience in data engineering or a related field.
• Strong knowledge of data architecture, data modeling, and database design.
• Experience with big data technologies such as Hadoop, Spark, and NoSQL databases.
• Proficiency in programming languages such as Python, Java, or Scala.
• Excellent problem-solving skills and ability to work collaboratively with cross-functional teams.
• Strong understanding of data quality, data governance, and data security principles.