Associate Technical Architect

What you'll do

• 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
• 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 implementation of data contracts, schema management, metadata enrichment, data quality frameworks, reusable transformation patterns
• Enable discoverability, trust, and operational reliability of enterprise data assets
• Support implementation of semantic and business-consumption layers that simplify enterprise data access
• Collaborate with analytics and BI teams to enable standardized business metrics, reusable dimensions, and governed KPI definitions
• Contribute to semantic modeling and metadata integration initiatives supporting self-service analytics and AI consumption
• Assist in improving enterprise data usability, consistency, and discoverability across platforms
• Develop and optimize solutions leveraging GCP-native services including BigQuery, Dataflow, Dataproc, DBT, Pub/Sub, Cloud Storage, Cloud Composer (Airflow), Cloud SQL
• Build scalable ETL/ELT frameworks and real-time streaming pipelines
• Optimize data processing performance, reliability, scalability, and cost efficiency
• Implement CI/CD pipelines and engineering automation for data platform delivery
• Build AI-ready data pipelines and scalable feature engineering workflows supporting enterprise AI initiatives
• Support integration with Vertex AI, BigQuery ML, Vector databases, LangChain, Generative AI Studio
• Contribute to implementation of RAG architectures, semantic search, 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 on modern engineering best practices
• Ensure adherence to coding standards, architecture guidelines, and operational best practices
• Drive engineering quality through automated testing, observability, monitoring, and performance optimization
• Collaborate with architects, product owners, analysts, and client stakeholders to ensure successful delivery outcomes
• Implement data governance, lineage, monitoring, and observability frameworks
• Support enforcement of enterprise standards around security, reliability, scalability, and operational readiness
• Contribute to platform monitoring, incident management, and continuous improvement initiatives
• Ensure production readiness of pipelines and data services through robust testing and validation processes

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field
• 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
• Strong 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
• Strong 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
• GCP certifications such as Professional Data Engineer preferred

Tech stack