Merkle
Leaddataengineer
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“Lead data engineer at Merkle. Skills: GCP, Data Engineering, BigQuery, Dataflow. Design, develop, and optimize scalable cloud-native data platforms. Implement robust batch, streaming, and event-driven data processing solutions”
Industry & Context.
problem-solving, debugging, and performance optimization skills
What They're Looking For.
Must Have
7+ years of experience in data engineering and cloud-native data platform development, Minimum 4+ years of hands-on experience delivering enterprise-scale solutions on GCP, expertise in building scalable batch and streaming data pipelines, Experience working on modern enterprise data platforms supporting analytics, AI/ML, and GenAI use cases, Good understanding of semantic layer concepts, reusable data models, and governed data consumption patterns, Experience working within large-scale data modernization and cloud transformation initiatives, problem-solving, debugging, and performance optimization skills, Proven ability to lead engineering teams and collaborate across architecture, product, and business functions, Excellent communication and stakeholder management skills
Nice to Have
GCP certifications such as Professional Data Engineer preferred
What You'll Do.
and optimize scalable cloud-native data platforms
Implement robust batch
and event-driven data processing solutions
Collaborate with Enterprise Architects on target-state architecture
Modernize legacy data ecosystems
Support implementation of ingestion
and orchestration frameworks
Develop reusable and domain-oriented data products
Implement scalable and modular data pipelines
Contribute to data contracts
data quality frameworks
Enable discoverability
and operational reliability of enterprise data assets
Support implementation of semantic and business-consumption layers
Collaborate with analytics and BI teams on standardized business metrics
Contribute to semantic modeling and metadata integration
Assist in improving enterprise data usability
Develop and optimize solutions leveraging GCP-native services
Build scalable ETL/ELT frameworks and real-time streaming pipelines
Optimize data processing performance
Implement CI/CD pipelines and engineering automation
Build AI-ready data pipelines and scalable feature engineering workflows
Support integration with AI/ML platforms and tools
Contribute to RAG architectures
and AI-assisted data interaction patterns
Partner with AI/ML teams to operationalize scalable ML and GenAI workflows
Lead day-to-day engineering activities across multiple data engineering workstreams
Guide and mentor junior and mid-level data engineers
Ensure adherence to coding standards
architecture guidelines
and operational best practices
Drive engineering quality through automated testing
and performance optimization
Collaborate with architects
and client stakeholders
Implement data governance
and observability frameworks
Support enforcement of enterprise standards around security
and operational readiness
Contribute to platform monitoring
and continuous improvement initiatives
Ensure production readiness of pipelines and data services
How You'll Work.
Team & Collaboration
Work closely with Enterprise Architects, platform leaders, and cross-functional engineering teams; Collaborate with Enterprise Architects to translate target-state architecture; Collaborate with analytics and BI teams; Collaborate with architects, product owners, analysts, and client stakeholders; Proven ability to lead engineering teams and collaborate across architecture, product, and business functions
Communication Scope
Excellent communication and stakeholder management skills
Full Job Description
**Job Description:** ## **Title:** Lead data engineer **DCF Level:** L40 **About the Role** We are seeking a highly skilled and delivery-focused Lead GCP Data Engineer to support the design, development, and implementation of next-generation enterprise data and AI platforms on Google Cloud Platform (GCP). This role will work closely with Enterprise Architects, platform leaders, and cross-functional engineering teams to build scalable, reusable, and AI-ready data foundations that enable advanced analytics, intelligent automation, and enterprise AI adoption. The ideal candidate combines strong hands-on expertise in cloud-native data engineering, modern data platform development, semantic data enablement, and scalable pipeline engineering with the ability to lead engineering teams and drive high-quality delivery across multiple initiatives. This role is expected to play a critical leadership position within the engineering organization by driving implementation excellence, mentoring teams, and operationalizing modern data architecture patterns. **Key Responsibilities** **1\. Enterprise Data Platform Engineering** * Design, develop, and optimize scalable cloud-native data platforms and pipelines on GCP. * Implement robust batch, streaming, and event-driven data processing solutions supporting enterprise analytics and AI use cases. * Collaborate with Enterprise Architects to translate target-state architecture into scalable engineering implementations. * Contribute to modernization of legacy data ecosystems into reusable, governed, and AI-ready cloud platforms. * Support implementation of scalable ingestion, transformation, serving, and orchestration frameworks. **2\. Data Product Engineering** * Develop reusable and domain-oriented data products aligned with data mesh and data-as-a-product principles. * Implement scalable and modular data pipelines supporting multiple downstream consumers including analytics, AI/ML, and operational applications. * Contribute to implemen
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