JOB RESPONSIBILITIES:
Design, build, and maintain ETL/ELT pipelines for automated data ingestion, transformation, and validation.
Establish a centralized data storage system, starting with lightweight solutions and scaling toward cloud-based infrastructure.
Collaborate with analysts and business teams to translate reporting needs into scalable, reusable data models.
Implement data quality checks, logging, and monitoring to ensure reliability and transparency of data flows.
Refactor existing Python/Jupyter workflows into structured, production-ready codebases.
Evaluate and recommend tools for data warehousing, orchestration, and cloud migration.
Define and promote best practices in version control, documentation, and reproducibility.
JOB REQUIREMENTS:
Strong proficiency in Python (pandas, PySpark, or similar libraries for data processing).
Solid understanding of SQL and experience with relational databases.
Hands-on experience designing or maintaining ETL pipelines and workflow orchestration tools (e.g., Airflow, Prefect, Luigi).
Good grasp of data modeling, schema design, and data governance principles.
Experience handling large datasets and optimizing data performance.
Comfortable working in environments with minimal existing infrastructure and building systems from scratch.
Strong analytical thinking, problem-solving skills, and ability to work with both technical and non-technical stakeholders.
NICE TO HAVE:
Experience with cloud platforms (AWS, GCP, or Azure) and cloud-native data services.
Knowledge of data warehousing technologies (Snowflake, BigQuery, Redshift, or Databricks).
Familiarity with CI/CD pipelines, Docker, or containerized workflows.
Exposure to business intelligence tools (Power BI, Tableau, or similar).
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