Software Engineer, Data Foundations

Accountabilities:

• • 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.

  Requirements:

  • 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.

  Benefits:

  • 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.