Data Engineering Manager

Role & responsibilities

• Hire, mentor and manage a team of data engineers, fostering a culture of innovation, collaboration and continuous learning in data engineering best practices.
• Design, develop and maintain scalable ETL (Extract, Transform, Load) pipelines to efficiently process and integrate structured and unstructured data from multiple sources.
• 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 data 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.
• Work closely 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.

Preferred candidate profile

• Minimum 8 years of relevant work experience, with at least 5 years in a leadership role managing data engineering teams and large-scale data infrastructure.
• Bachelors degree (B.E./B.Tech) in Computer Science, IT, or a related field or Diploma in Data Science/Data Engineering.
• Strong expertise in ETL development, data warehousing and real-time data processing frameworks.
• Hands-on experience with SQL, Python, Spark, Kafka and cloud platforms (AWS, Azure, Google Cloud).
• Proficiency in database technologies (PostgreSQL, MySQL, MongoDB, Snowflake, Big Query) and distributed computing frameworks.