Data Engineer (Middle to Senior level)

We are looking for a passionate and detail-oriented Data Engineer to join our team. In this role, you will contribute to developing scalable internal data tools and modern data platform components. You will help build a robust data infrastructure that powers analytics across the organization.

Key Responsibilities

• Collaborate in designing and implementing scalable data platform components, including Data Warehouse, Data Lake, and Feature Store
• Participate in the deployment, automation, and monitoring of data platform infrastructure
• Design, implement, and maintain efficient ETL data pipelines
• Develop and support internal web-based tools for data access and operations
• Work closely with data analysts, scientists, and engineers to understand data requirements and ensure high data quality
• Continuously research and evaluate new tools, frameworks, and technologies related to data engineering

Requirements

• Background in Computer Science, Data Engineering, or a related technical field
• At least 2 years of experience in a data engineering or related role.
• Hands-on experience with Apache Spark for distributed data processing
• Familiarity with Apache Airflow for data workflow orchestration
• Understanding of Hadoop ecosystem and large-scale data processing frameworks
• Strong knowledge of Data Warehouse (e.g., star schema, snowflake schema) and Data Lake concepts
• Basic understanding of RDBMS and NoSQL databases, and ability to write simple to moderately complex SQL queries
• Familiarity with Unix environments, distributed computing, and version control systems (e.g., Git)
• Familiarity with CI/CD pipelines, Docker, and infrastructure-as-code tools such as Terraform or Ansible
• Basic experience developing internal web-based tools, with a focus on frontend development using ReactJS

Soft Skills:

• Strong collaboration and problem-solving skills, with a proactive approach to working with cross-functional teams.
• Ability to manage time effectively and prioritize tasks in a fast-paced environment.
• Willingness to learn and adapt to new tools, technologies, and business needs

Nice to have:

• Experience with cloud platforms such as AWS, GCP, or Azure
• Knowledge of feature engineering and ML pipelines
• Exposure to tools like dbt, Trino, Starrock, Datahub
• Awareness of data governance and security best practices