Data Engineer

Design & Build Robust Pipelines: Develop, deploy, and maintain scalable, highly reliable, and idempotent ELT data pipelines using Python and orchestration tools like Airflow.

Own the Data Model: Lead data transformation and modeling efforts within our cloud data warehouse (e.g., Snowflake, AWS) using dbt, ensuring adherence to modern analytics engineering best practices (modularity, DRY principles, and clear separation of staging and data marts).

Expand the Semantic Layer: Architect and grow our centralized semantic layer to establish a "single source of truth" for business metrics, powering both traditional BI dashboards and upcoming AI initiatives.

Champion Data Quality & Reliability: Implement rigorous data validation, testing, and monitoring to ensure data integrity and build trust with downstream consumers.

Enable Self-Service Analytics: Design intuitive, long-term data infrastructure solutions that empower business stakeholders, analysts, and developers to easily and independently query organizational data.

Cross-Functional Collaboration: Partner closely with Backend developers, BI analysts, architects, and business decision-makers to translate complex business requirements into efficient technical architectures.

Bachelor’s degree in CS or other relevant field.

3+ years of proven experience as a Data Engineer, Analytics Engineer, or similar role.

Strong proficiency in Python, particularly for data processing and pipeline orchestration.

Experience in Data Modeling using dbt or equivalent.

Experience with Data Warehouse technologies like Snowflake, BigQuery, Redshift ,etc.

Experience with Orchestration platforms like Airflow, Luigi, Dagster, etc.

Experience with Semantic Data Layer technologies like MetricFlow, Cube or others.

Experience in working and delivering end-to-end projects independently.

Experience with at least one cloud provider, preferably AWS.

Strong written and verbal skills in Technical English.

Experience with ELT platforms like dlt, Fivetran, Airbyte, etc.