• Develop and maintain scalable ETL (Extract, Transform, Load) pipelines that ingest, clean, and structure data from various sources (internal and external).
• Data Infrastructure: Design, implement, and maintain cloud-based data storage solutions (e.g., GCP, AWS, Azure,
• Snowflake, etc.), ensuring data availability, reliability, and scalability.
• Collaborate with data governance teams to ensure that all data adheres to quality standards, security protocols, and
• compliance with relevant regulations (e.g., GDPR, CCPA).
• Continuously monitor and optimize data pipelines, database performance, and storage solutions to ensure efficient data
• processing and access.
• Work closely with data scientists, analysts, and business intelligence teams to understand data requirements and provide reliable data solutions.
• Automate recurring data tasks, such as data cleaning, transformation, and loading, to ensure the system operates
• smoothly and with minimal manual intervention.
• Design and implement database schemas, dimensional models, and data lakes to support analytics and business
• intelligence efforts.
• Create and maintain detailed documentation for data pipelines, infrastructure, and workflows to ensure transparency and
• ease of collaboration.
• Implement and maintain data security measures such as role-based access control (RBAC), encryption, and backups to
• protect sensitive data.
• Set up monitoring tools and proactively identify and resolve data-related issues (performance bottlenecks, failures, etc.).

REQUIREMENTS

• Bachelor’s degree in a field such as computer science, information technology, statistics, or mathematics.
• A minimum of 3 years of experience in data engineering or a related field.
• Strong experience with SQL and relational databases (e.g., PostgreSQL, MySQL).
• Understanding of data modeling, data lakes, and data warehouses.
• Knowledge of data warehousing solutions such as BigQuery, Snowflake, or Redshift.
• Proficiency with cloud platforms like AWS, or Azure , Google Cloud Platform (GCP) as preferred .
• Proficiency in programming languages such as Python.
• Experience with big data technologies such as Apache Hadoop, Spark, Kafka, etc..
• Familiarity in ETL tools like Airflow, Talend, or Apache NiFi.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• Ability to analyze and optimize large, complex datasets. Aware of Business acumen and objectives oriented.
• Strong problem-solving skills and ability to troubleshoot complex data and infrastructure issues.
• Excellent communication skills to work effectively with cross-functional teams (e.g., analytics, product, marketing and

growth).

• Familiarity with CI/CD practices for data pipelines.

Please refer the Job description for details