About The Role
This role focuses on designing, building, and optimizing the core data infrastructure and pipelines that power both business intelligence and machine learning applications. The engineer will scale reliable data pipelines to ingest, transform, and store large-scale datasets from diverse sources.
Collaborating closely with data scientists, product managers, and software engineers, this role ensures data availability, quality, and governance across the entire data ecosystem.
Key Responsibilities
- Design, develop, and maintain robust batch and real-time data pipelines using Python, SQL, and Apache Spark
- Build and optimize data warehousing solutions in Snowflake or BigQuery, focusing on performance, cost optimization, and schema design
- Orchestrate complex data workflows utilizing tools like Apache Airflow, Prefect, or Dagster
- Implement data quality frameworks and monitoring solutions to ensure data reliability, completeness, and consistency across production environments
- Collaborate with backend engineering teams to integrate transactional databases and event streams like Kafka or Kinesis into the central data lake
- Establish software engineering best practices within the data team, including version control, CI/CD, and infrastructure-as-code using Terraform
What We Are Looking For
- 3–6 years of experience in data engineering, data platform engineering, or a related software development role
- Strong proficiency in Python and advanced SQL, with experience writing optimized analytical queries and ETL/ELT scripts
- Hands-on experience with modern data stack technologies such as Snowflake, dbt, Airflow, and major cloud platforms like AWS or GCP
- Solid understanding of data modeling techniques, including dimensional modeling, Star schemas, and data lake architecture
- Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience
- Bonus: Experience with streaming architectures (Kafka, Flink), Kubernetes, or building infrastructure via Terraform
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