Software Engineer, Data Foundations in India at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineer, Data Foundations based in India.

This role offers the opportunity to build the foundational data infrastructure that powers analytics and AI at enterprise scale. You will work across the full data lifecycle, from ingestion and transformation to semantic modeling and reliable data access. The position combines software engineering, data architecture, domain modeling, and AI-enabled development. You will design production-grade batch and streaming systems while taking ownership of data quality, observability, and documentation. Your work will help business teams and AI agents access trusted, well-structured data with greater consistency and reliability. You’ll join a technically ambitious environment that values long-term platform health, scalable architecture, and rapid iteration.

• Design and build scalable, fault-tolerant data pipelines that transform raw source data into trusted, well-documented datasets across both batch and streaming architectures.
• Own data quality and observability end-to-end by implementing monitoring, alerting, automated testing, validation, and comprehensive documentation across pipelines and data models.
• Build and continuously evolve semantic and metric layers that provide clear context and guardrails for reliable data access by both human analysts and AI agents.
• Architect enterprise-grade datasets using appropriate modeling approaches, including dimensional modeling, Data Vault, or One Big Table principles, with robust grain definitions and key strategies.
• Design and scale data platforms capable of operating in complex, multi-tenant environments, including architectures supporting multi-region replication.
• Leverage generative AI development frameworks and tools to accelerate engineering workflows and enable more intelligent analytics capabilities.
• Partner with business stakeholders, product leaders, AI teams, and other technical teams to translate ambiguous requirements into scalable and production-ready technical solutions.
• Contribute to the long-term technical health of the data platform while balancing architectural quality with the need for rapid delivery and iteration.

• 6+ years of software engineering experience, or equivalent practical experience, with significant exposure to data architecture, data engineering, or system design.
• Demonstrated experience architecting complex data models, including grain selection, key strategy, schema design, and scalable data architecture.
• Proven ability to design and scale data platforms in multi-tenant environments and work with multi-region data architectures.
• Strong programming skills in Python, Java, or Scala, supported by solid software engineering fundamentals.
• Deep knowledge of modern data modeling paradigms, particularly dimensional modeling, Data Vault, or One Big Table approaches.
• Hands-on experience designing and implementing both batch and streaming data pipelines.
• Strong understanding of data quality, observability, testing, monitoring, alerting, and documentation practices.
• Ability to design semantic and metric layers that make enterprise data easier and more reliable to consume.
• Experience working directly with business teams, product leaders, or clients and translating ambiguous business needs into effective technical solutions.
• Familiarity with generative AI development tools and frameworks, particularly those used to accelerate software engineering and intelligent analytics.
• Strong problem-solving, communication, collaboration, and systems-thinking skills, with the ability to operate effectively in a fast-paced technical environment.

• Remote work: Fully remote opportunity based in India.
• Flexible working approach: A structured work environment designed to support remote employees and different working styles.
• Health and wellbeing: Benefits and programs supporting physical, mental, emotional, and financial wellbeing.
• Work-life balance: Programs and benefits designed to help employees balance professional and personal priorities.
• Professional growth: Opportunities to develop technical expertise and take on challenging, high-impact engineering work.
• Learning opportunities: Exposure to enterprise-scale data infrastructure, AI, semantic modeling, and modern data engineering practices.
• Collaborative culture: Work alongside engineering, business, product, and AI teams in a growth-oriented environment.
• Meaningful technical impact: Build foundational systems that enable reliable analytics and AI-powered data experiences at scale.