JOB DESCRIPTION

We are seeking a highly skilled and motivated Data Engineer to join our backend engineering team. This role will play a critical part in developing and optimizing data pipelines to support Order Management Systems (OMS), inventory management, and enterprise-level services. The ideal candidate will have a strong background in data engineering, ETL processes, and cloud-based data solutions to ensure seamless integration between backend services and data infrastructure.

QUALIFICATIONS & SKILLS:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• 5+ years of experience in data engineering, backend systems, or related fields.
• Proficiency in SQL, Python, or Scala for data processing and manipulation.
• Experience with ETL tools (Apache Airflow, DBT, or similar) and data pipeline frameworks.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and their data services.
• Experience with relational and NoSQL databases (SQL Server, Oracle 11g/12c, Postgres, MongoDB, etc.).
• Understanding of big data technologies (Spark, Hadoop) is a plus.
• Strong problem-solving skills and ability to work in a fast-paced environment.
• Hands-on experience with Databricks for data engineering and analytics.

PREFERRED QUALIFICATIONS:

• Experience working with Order Management Systems (OMS) or inventory management.
• Knowledge of Kafka, RabbitMQ, or similar messaging systems.
• Hands-on experience with data warehousing solutions (Redshift, Snowflake, BigQuery).
• Exposure to DevOps and CI/CD practices for data engineering.
  If you are passionate about data engineering and want to contribute to enterprise-level services, we encourage you to apply!
  

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• Design, develop, and maintain scalable data pipelines and ETL processes to support OMS, inventory, and enterprise applications.
• Collaborate with backend engineers to integrate data solutions with microservices and APIs.
• Ensure data accuracy, consistency, and availability for analytics, reporting, and operational use cases.
• Optimize and improve database structures, queries, and indexing strategies for performance and scalability.
• Implement data governance policies, security measures, and compliance requirements.
• Monitor and troubleshoot data pipeline performance, resolving issues proactively.
• Work with stakeholders to understand business needs and translate them into technical solutions.