Data Engineer

About The Role

The role owns the design, implementation, and scaling of core data infrastructure and reliable ETL pipelines that power analytics and downstream data consumers.

The data engineering team works closely with analytics engineers, product managers, and software teams to ensure data flows securely, efficiently, and accurately across all systems.

Key Responsibilities

• Design, build, and maintain scalable data pipelines and ETL processes using Python, SQL, and Apache Spark
• Manage cloud data warehousing solutions such as Snowflake, BigQuery, or AWS Redshift to optimize query performance and storage costs
• Implement data modeling best practices using dbt (data build tool) for modular and maintainable transformation logic
• Monitor data pipeline health and data quality, setting up automated alerts for anomalies and schema changes
• Collaborate with security and engineering teams to ensure data governance, privacy compliance, and reliable access controls
• Write clean, version-controlled code with thorough testing and CI/CD automation for all data infrastructure components
  
  

What We Are Looking For

• 3–6 years of experience in data engineering, backend development, or a closely related technical domain
• Advanced SQL and Python programming skills for data manipulation and pipeline development
• Hands-on experience with modern cloud data warehouses and orchestration tools such as Airflow, Prefect, or Dagster
• Solid understanding of data modeling concepts (dimensional modeling, star/snowflake schemas) and dbt
• Bonus: Experience with real-time streaming architectures (Kafka, Flink) and infrastructure-as-code tools (Terraform)