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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing data ingestion and transformation pipelines on Google Cloud, ensuring data quality and scalability. Proficient in optimizing costs and collaborating with data teams to deliver actionable insights.
Highest-signal resume keywords
Data Ingestion With DataflowReal-Time Ingestion With Pub/SubExperience With BigQueryAdvanced SQL (BigQuery SQL)Orchestration With Cloud Composer
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data IngestionETL/ELT PipelinesData ModelingQuery OptimizationBatch ProcessingStreaming ProcessingSQL OrchestrationPythonGitCloud Functions
Tools & Technologies
Google CloudBigQueryDataflowDataprocPub/SubCloud StorageCloud ComposerDataformDbtCloud Run
Industry Keywords
Healthcare SystemsPEPEMREHRRISLISScheduling SystemsReferral Management Systems
Tech Stack
Tools & technologiesAirflowApacheAWSBigQueryCloudETLMySQLOraclePythonSQL
About the role
Key responsibilities & impact- Design and implement data ingestion, transformation, and orchestration pipelines on Google Cloud
- Ensure the quality, reliability, and scalability of batch and streaming data flows
- Optimize storage and processing costs in BigQuery, Dataflow, Dataproc, and Pub/Sub
- Collaborate with data scientists and analysts to deliver consumption-ready data
Requirements
What you’ll need- Data ingestion with Dataflow (Apache Beam) for scalable ETL/ELT pipelines, including extraction from external sources such as Oracle on OCI and on-premises SQL Server
- Real-time ingestion with Pub/Sub and batch ingestion with Storage Transfer, including sources such as AWS S3 buckets
- Integration with Oracle, SQL Server, and MySQL via JDBC, Dataflow templates, or custom extraction
- Experience with BigQuery, including data modeling, query optimization, partitioning, and clustering
- Experience using Cloud Storage as a layered data lake (raw, bronze, silver, gold)
- Dataform or dbt for SQL orchestration in BigQuery, along with dimensional modeling best practices
- Batch and streaming processing with Beam and streaming SQL
- Orchestration with Cloud Composer (Airflow) or Workflows
- Integration with Cloud Functions and Cloud Run for serverless tasks
- Advanced SQL (BigQuery SQL), Python (Dataflow, automation, ETL), and Git with CI/CD
- Familiarity with Dataproc, Bigtable, and Firestore is desirable
- Familiarity with Dataplex and Data Catalog for data documentation and cataloging is desirable
- Familiarity with healthcare systems, including PEP/EMR/EHR, RIS, LIS, and scheduling and referral management systems, is desirable