Data Engineer

Your tasks

• Design, build, and maintain scalable and reliable data pipelines to support analytics, ML models, and business reporting.
• Collaborate with data scientists and analysts to ensure data is available, clean, and optimized for downstream use.
• Implement data quality checks, monitoring, and validation processes.
• Design efficient ETL/ELT workflows and integrate multiple data sources (databases, APIs, third-party tools) into centralized storage (data lakes/warehouses).
• Support cloud-based infrastructure for data storage and retrieval.
• Monitor, troubleshoot, and optimize existing pipelines for large-scale, real-time data flows.
• Apply best practices for query optimization and cost-efficient storage.
• Partner with product, engineering, and business stakeholders to gather requirements.
• Document workflows, schemas, and processes while supporting a culture of reliability, governance, and security.

Requirements

• Proficiency in Python and SQL for data engineering tasks.
• Strong understanding o f ETL/ELT processes, data warehousing, and data modeling.
• Hands-on experience with cloud platforms(AWS, GCP, or Azure) and data storage solutions (BigQuery, Redshift, Snowflake, etc.).
• Familiarity with data orchestration tools Airflow, Airbyte is a must.
• Experience with containerization & deployment tools (Docker, Kubernetes) is a plus.
• Knowledge of data governance, security, and best practices for handling sensitive data.Familiarity to work with Git and GitHub.
• Dataform is a must
• Strong skills in eliciting requirements from cross-functional stakeholders and translating them into actionable data engineering tasks.

• Preferably total 4 years of experience
• 2+ years in data engineering, building and maintaining data pipelines.
• 2+ years of SQL and Python development in production environments.
• Experience working in Fast-growing startup environments is a plus.
• Exposure to real-time data processing frameworks (Kafka, Spark, Flink) is a plus.