Design, build, and maintain data infrastructure that enables reliable ingestion, transformation, storage, and availability of data for analytics, data science, and artificial intelligence in enterprise environments.

• Design and maintain robust, scalable data pipelines (ETL / ELT).
• Integrate multiple data sources, including APIs, databases, files, and streaming systems.
• Optimize data storage, performance, and query efficiency.
• Ensure data quality, lineage, security, and reliability.
• Implement data models to support analytics and business intelligence use cases.
• Collaborate closely with Data Scientists, AI Engineers, and Analytics teams.
• Document data flows, schemas, and operational processes.
• Participate in continuous improvement of data architecture and scalability.

Technical Requirements

• Advanced SQL skills and strong data modeling experience.
• Proficiency in Python or Scala for data processing.
• Experience with data storage systems such as PostgreSQL, MySQL, BigQuery, Snowflake, or Redshift.
• Hands-on experience with ETL/ELT tools (Airflow, dbt, Fivetran, or similar).
• Knowledge of cloud-based data architectures (AWS, Azure, or GCP).
• Solid understanding of Data Lake and Data Warehouse concepts.

Experience

• 2+ years building and maintaining production-grade data pipelines.
• Experience working with high-volume data or multiple heterogeneous data sources.
• Experience collaborating in multidisciplinary teams.

Core Competencies

• Structured thinking with a strong focus on data quality.
• Ability to anticipate and address scalability challenges.
• High sense of ownership over data reliability and availability.