Design, build, and maintain scalable data pipelines, integrations, and ETL/ELT workflows on Google Cloud Platform using BigQuery, Airflow, and Cloud Data Fusion.
Ingest data from internal and external sources into the enterprise data lake, with primary ownership of the bronze layer within a medallion architecture.
Analyze legacy systems, databases, workflows, and transformation logic to understand current-state processes, dependencies, and opportunities for modernization.
Document current-state data flows and system behavior and develop recommendations for process improvement, architecture simplification, and legacy-to-cloud modernization.
Use strong SQL skills to analyze source data, validate transformations, understand embedded business logic, and support data analysts with trusted datasets.
Partner with business stakeholders and internal customers to gather requirements, clarify needs, support testing, and translate business needs into scalable technical solutions.
Work independently to identify next steps, resolve technical and process issues, and collaborate with cross-functional teams to deliver high-quality data solutions.
Apply data quality, governance, documentation, and security standards while continuously improving pipeline reliability, scalability, and performance.
Qualifications
Bachelor’s degree in Data Engineering, Data Science, Computer Science, Information Technology or a related field.
3+ years of experience in data engineering, cloud data platforms, data integration, or related roles.
Hands-on experience building data pipelines in Google Cloud Platform.
Strong working knowledge of BigQuery, Cloud Composer, and Cloud Data Fusion.
Strong SQL skills, including the ability to analyze legacy systems, investigate data issues, validate transformations, and support analytics users.
Knowledge of Python and Data Engineering packages such as PySpark and Airflow
Experience building and maintaining ETL/ELT pipelines for structured and semi-structured data.
Familiarity with data lake concepts and medallion architecture, especially bronze-layer ingestion patterns.
Familiarity with data governance, metadata, lineage, and data quality practices.
Strong problem-solving, analytical, communication, and self-management skills.
Experience working in cross-functional or global teams.
