Description

Key Skills: Python, Apache Airflow, ETL/ELT, dbt Core, SQL, Data Warehousing, Google Cloud Platform, Data Architecture, Data Governance, Data Pipelines

Good to Have Skills: Experience with Google Cloud Storage (GCS) and Cloud Composer, Kafka or other event streaming platforms, data governance and cataloging tools, CI/CD and observability tooling for data pipelines, cloud-native orchestration, data life cycle management including ingestion, transformation, quality, governance, retention, and archival.

Roles & Responsibilities:

• Lead the architecture and strategy for ETL/ELT pipelines using Python, Airflow, and dbt Core across the Enterprise Data platform.
• Define and drive data management standards across the full data life cycle including ingestion, transformation, quality, governance, retention, and archival.
• Partner with Product, Architecture, and business stakeholders on data strategy and roadmap planning.
• Mentor engineers across multiple experience levels and set engineering best practices for pipeline development.
• Drive adoption of cloud-native, GCP-based orchestration such as Cloud Composer, and storage such as GCS.
• Own data governance, cataloging, and lineage strategy for the organization.
• Lead root-cause analysis and performance optimization for large-scale data pipelines.
• Drive CI/CD automation and observability for data pipelines across the platform.

Experience Required: 8+ years of experience in data engineering with leadership responsibility. Deep expertise in Python, Apache Airflow, ETL/ELT architecture at enterprise scale, and dbt Core. Proven experience architecting large-scale data pipelines and data platforms. Experience with cloud-native data platforms, with preference for Google Cloud Platform (GCP). Experience mentoring engineers and leading technical initiatives. Strong communication skills for partnering with stakeholders across the business.

Key Skills

Education

Any Graduate