Lead Data Engineer

Company Description

BLEND360 is an acclaimed, forward-thinking Data, Digital Marketing, & AI Solutions Company, dedicated to fueling remarkable outcomes for our Fortune 500 clients. Our trajectory is one of continuous expansion, emerging at the crossroads of cutting-edge analytics, data proficiency, technology, and digital marketing excellence.

Job Description

We are seeking a Lead Data Engineer to support a large-scale healthcare data platform initiative focused on modernizing data ingestion, automation, and semantic layer capabilities within Google Cloud Platform (GCP). This role will play a critical part in building scalable, reliable, and business-ready data solutions that enable enterprise analytics and reporting across the organization. 

The ideal candidate brings strong hands-on engineering expertise, deep knowledge of cloud-native data platforms, and experience leading technical implementation efforts in complex enterprise healthcare environments. 

What You’ll Do 

• Design, build, and optimize scalable data pipelines supporting enterprise healthcare analytics and reporting use cases  

• Lead automation efforts across ingestion, transformation, orchestration, and operational monitoring workflows  

• Develop and maintain ingestion frameworks for structured and semi-structured healthcare data sources  

• Build and expand semantic data layers within GCP and BigQuery to support consistent, trusted business reporting and analytics consumption  

• Design scalable ELT/ETL workflows leveraging modern cloud-native architecture patterns  

• Partner closely with architects, analytics teams, and business stakeholders to translate data requirements into scalable technical solutions  

• Optimize BigQuery performance, partitioning, and cost management strategies  

• Implement engineering best practices for testing, observability, reliability, and deployment automation  

• Support data governance, data quality, and metadata management initiatives across the platform  

• Mentor junior engineers and provide technical leadership across delivery teams  

Qualifications

Required Skills 

• Strong expertise in Google Cloud Platform (GCP), particularly:  

  • BigQuery

  • Cloud Storage

  • Cloud Composer / orchestration tooling

  • Dataflow or modern ingestion frameworks  

• Experience designing and building scalable data ingestion and pipeline automation frameworks  

• Strong understanding of semantic layer development and dimensional modeling concepts  

• Advanced SQL and Python development skills  

• Experience with ETL/ELT pipeline design and orchestration  

• Familiarity with CI/CD, infrastructure automation, and cloud engineering best practices  

• Strong understanding of data quality, observability, and operational reliability principles  

Preferred Experience 

• Experience working with healthcare data ecosystems and regulated enterprise environments  

• Familiarity with healthcare data standards or payer/provider analytics platforms  

• Experience supporting enterprise BI and analytics initiatives  

• Exposure to dbt, Looker, or semantic modeling frameworks is a plus  

Required Experience 

• 7+ years of experience in data engineering or cloud data platform development  

• Proven experience delivering enterprise-scale cloud data solutions in GCP environments  

• Experience leading technical workstreams or mentoring engineering teams  

• Consulting or client-facing experience strongly preferred  

What Makes You Successful 

• You are passionate about building scalable and maintainable cloud-native data solutions  

• You balance hands-on engineering execution with technical leadership and collaboration  

• You communicate effectively with both technical and business stakeholders  

• You thrive in fast-paced, delivery-focused environments  

• You bring strong ownership, curiosity, and continuous improvement mindset to your work  

Additional Information

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The starting pay range for this role is $150k - $160k. Actual compensation within the range will be dependent on several factors including but not limited to relevant experience, skills, certifications, training, and location. It is not typical for an i