Own and architect the end-to-end reporting lifecycle in the diamond matching project
Inherit a replicated production environment
Transform raw Python application data into a high-performance analytics layer
Bridge the gap between backend data structures and business-ready visualizations

Requirements

Required experience
- Building and maintaining a robust, end-to-end data and analytics platform.
- Developing and optimizing data pipelines to move data from online environments to the data warehouse and reporting layers.
- Writing high-quality, production-ready code in SQL and Python, utilizing libraries such as pandas and SQLAlchemy.
- Troubleshooting and resolving issues related to cloud data configuration, synchronization, and data latency.
- Managing and processing diverse data types, including both structured and unstructured data, such as JSONB.
- Designing, deploying, and maintaining automated business intelligence dashboards and internal monitoring tools.
Required skills
- Advanced SQL knowledge (window functions, CTEs, optimization queries)
- Advanced knowledge of Data Build Tool (dbt) (or similar)
- Working knowledge of code versioning and peer code reviewing process.
- Working knowledge of deduplication and normalization strategies
- Working knowledge of Cloud infrastructure
- Working knowledge of Python coding principles and data libraries
- Knowledge of ORMs such as SQLAlchemy.
Extras points for
- Knowledge of ML tooling, such as MLFlow and Sagemaker.
- Experience with AWS.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLpandasSQLAlchemyData Build Tool (dbt)data pipelinesdata warehousedata latencydeduplicationnormalization