{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Software & Platform Engineering Lead”, “description”: “
Requirements
- 10+ years in data engineering, data warehousing, analytics or similar roles
- Strong expertise in Snowflake architecture and AWS cloud services (incl API interfaces)
- Experience leading and mentoring engineering teams, particularly distributed or remote teams
- Strong proficiency in programming languages like Python
- Proven experience designing and implementing enterprise data warehouse solutions
- Proven expertise in building and optimizing ETL pipelines using tools like SQL, Python and dbt
- Experience with AI development tools and platforms (e.g., GPT APIs, Copilot) and AI agent frameworks
- Architect, designing and implementing robust and scalable CI/CD pipelines across diverse cloud (AWS, Azure) environments
- Familiarity with data governance, data quality frameworks, and access controls
- Strong communication skills for partner engagement and cross-functional collaboration
What the job involves
- Echnical leadership: Guide and mentor the engineering team, making key technical decisions and providing a strategic framework and detailed technical guidance covering architectural patterns, tactical design patterns, best practices, and a clear implementation process
- Product development: Lead the technology assessment for new products/features, influencing design decisions to manage risks and develop mitigation plans
- Cloud Architecture: Design and build solutions on public cloud platforms like AWS or Azure, leveraging services for compute, storage, and networking
- Cross-functional collaboration: Work closely with product management, design, quality, and manufacturing teams to ensure a successful product launch and sustainment
- Lead AI integration: Work with leadership to implement the company's long-term AI strategy, identifying practical use cases for AI and translating product goals into actionable technical tasks. Lead the design, development, and deployment of AI‑enhanced software solutions and oversee the integration of AI capabilities into products and workflows
- Prototyping and testing: Develop and implement testing procedures, create product prototypes, and conduct performance testing to ensure requirements are met
- Problem-solving: Lead the resolution of technical issues and quality excursions that arise during the product development and production phases
- This is a hands‑on role involving deep technical work in Cloud Data Engineering, Analytics and ML
Company: Deepstreamtech
Location: London
Job Description:
Requirements
- 10+ years in data engineering, data warehousing, analytics or similar roles
- Strong expertise in Snowflake architecture and AWS cloud services (incl API interfaces)
- Experience leading and mentoring engineering teams, particularly distributed or remote teams
- Strong proficiency in programming languages like Python
- Proven experience designing and implementing enterprise data warehouse solutions
- Proven expertise in building and optimizing ETL pipelines using tools like SQL, Python and dbt
- Experience with AI development tools and platforms (e.g., GPT APIs, Copilot) and AI agent frameworks
- Architect, designing and implementing robust and scalable CI/CD pipelines across diverse cloud (AWS, Azure) environments
- Familiarity with data governance, data quality frameworks, and access controls
- Strong communication skills for partner engagement and cross-functional collaboration
What the job involves
- Echnical leadership: Guide and mentor the engineering team, making key technical decisions and providing a strategic framework and detailed technical guidance covering architectural patterns, tactical design patterns, best practices, and a clear implementation process
- Product development: Lead the technology assessment for new products/features, influencing design decisions to manage risks and develop mitigation plans
- Cloud Architecture: Design and build solutions on public cloud platforms like AWS or Azure, leveraging services for compute, storage, and networking
- Cross-functional collaboration: Work closely with product management, design, quality, and manufacturing teams to ensure a successful product launch and sustainment
- Lead AI integration: Work with leadership to implement the company’s long-term AI strategy, identifying practical use cases for AI and translating product goals into actionable technical tasks. Lead the design, development, and deployment of AI‑enhanced software solutions and oversee the integration of AI capabilities into products and workflows
- Prototyping and testing: Develop and implement testing procedures, create product prototypes, and conduct performance testing to ensure requirements are met
- Problem-solving: Lead the resolution of technical issues and quality excursions that arise during the product development and production phases
- This is a hands‑on role involving deep technical work in Cloud Data Engineering, Analytics and ML
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Posted: May 20th, 2026