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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 maintaining scalable data pipelines using Python, SQL, and Google Cloud Platform tools, ensuring data quality and integrity. Proven ability to mentor junior engineers and contribute to architectural decisions while optimizing data workflows for performance and cost efficiency.
Highest-signal resume keywords
Data Pipeline DevelopmentGoogle Cloud Platform (GCP)Python ProgrammingSQL ProficiencyData Warehousing Best Practices
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Design, develop, and maintain scalable, reliable, and efficient batch data pipelines using Airflow (Cloud Composer), Python, SQL, and dbt
- Build and maintain real-time ingestion pipelines that land event data from Pub/Sub into Cloud Storage and BigQuery using Dataflow
- Build and optimize dimensional data models and workflows in the analytics data warehouse
- Own assigned pipelines end to end, from design and development through testing, deployment, and production support
- Ensure data quality, integrity, security, and availability across the data ecosystem
- Partner with Product Managers, Data Scientists, Analysts, and Engineering teams to translate business requirements into technical solutions
- Contribute to data architecture discussions and long-term platform strategy
- Identify opportunities for process improvements and operational efficiencies
- Optimize data storage, processing, and orchestration for performance, scalability, and cost efficiency
- Monitor, troubleshoot, and resolve data pipeline and platform issues, and contribute to incident reviews
- Implement best practices for data governance, observability, testing, and CI/CD
- Mentor and support junior data engineers through technical guidance, code reviews, and knowledge sharing
- Contribute to architectural decisions and engineering best practices
- Participate in technical interviews and help grow a high-performing data engineering team
Requirements
What you’ll need- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related technical field
- 7+ years of experience in Data Engineering, Data Warehousing, or Big Data environments
- Advanced proficiency in Python and SQL
- Hands-on experience building and owning production-grade, end-to-end data pipelines
- Strong experience with Google Cloud Platform (GCP), including BigQuery, Cloud Storage, Pub/Sub, Dataflow, and Cloud Composer; AWS experience will also be considered
- Experience with workflow orchestration and transformation tools such as Airflow and dbt
- Experience building real-time or near-real-time data pipelines
- Strong understanding of data warehousing best practices, including relational and dimensional modeling
- Experience with Infrastructure as Code (IaC) tools such as Pulumi or Terraform
- Solid understanding of distributed data processing frameworks, including Spark
- Strong analytical, troubleshooting, and problem-solving abilities
- Must be authorized to work in the United States
- Applicants must be currently authorized to work in the United States on a full-time basis
- Employment/education will be verified
Benefits
Comp & perks- PTO
- Tuition reimbursement
- Wellness and employee support programs
- 401(k)
- Life and other insurance plans
- Medical, dental, and vision coverage options
- Equal opportunity employment
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 EngineeringData WarehousingBatch Data PipelinesReal-Time Data PipelinesWorkflow OrchestrationDimensional ModelingInfrastructure as Code (IaC)Distributed Data ProcessingData GovernanceCI/CD Implementation
Soft Skills
Analytical AbilitiesTroubleshooting SkillsProblem-Solving SkillsMentoringCollaboration
Tools & Technologies
AirflowCloud ComposerBigQueryCloud StoragePub/SubDataflowDbtPulumiTerraformSpark
Industry Keywords
Big DataData EcosystemData ArchitectureOperational EfficienciesTechnical Solutions