Software Engineer, Data Foundation

Accountabilities:
The Software Engineer, Data Foundation will be responsible for designing, developing, and maintaining scalable data platforms that enable reliable analytics, AI applications, and business decision-making. Design and build scalable, fault-tolerant data pipelines that transform raw data sources into trusted, well-documented datasets. Develop and maintain batch and streaming data architectures to support reliable data processing at enterprise scale. Own data quality, observability, and reliability by implementing monitoring, alerting, testing, and documentation practices across pipelines and data models. Build and evolve semantic and metric layers that provide clear context and enable accurate data exploration by analysts and AI systems. Architect well-structured datasets using advanced data modeling approaches, including dimensional modeling, Data Vault, and One Big Table principles. Define effective data structures, including grain selection, schema design, and key strategies to support scalable platforms. Develop enterprise data solutions using modern engineering practices and Generative AI frameworks to improve development efficiency and intelligent analytics capabilities. Collaborate with business teams, product leaders, and stakeholders to translate complex requirements into practical technical solutions. Contribute to continuous improvements in data platform architecture, performance, scalability, and long-term technical health. Requirements: The ideal candidate will have strong software engineering experience combined with deep expertise in data architecture, modeling, and scalable platform development. 6+ years of software engineering experience or equivalent practical experience, with significant exposure to data architecture or system design. Proven experience designing complex data models, including schema design, grain definition, key strategy, and large-scale data platform development. Strong programming skills in languages such as Python, Java, or Scala, with solid software engineering fundamentals. Deep knowledge of data modeling methodologies, including dimensional modeling, Data Vault, or One Big Table approaches. Experience building and managing batch and streaming data pipelines in complex environments. Experience designing scalable data solutions for multi-tenant platforms and enterprise systems. Ability to collaborate directly with business teams, product stakeholders, and external partners to solve ambiguous technical challenges. Strong understanding of data quality, documentation, testing, monitoring, and reliability practices. Familiarity with Generative AI development tools and frameworks is a plus. Strong analytical thinking, problem-solving skills, and ability to balance technical excellence with business priorities. Benefits: The role offers a flexible remote work environment and the opportunity to contribute to impactful technology initiatives within a collaborative engineering culture. Fully remote work opportunity for India-based candidates. Opportunity to build foundational data systems supporting analytics and AI innovation. Exposure to modern data engineering practices, scalable architectures, and Generative AI technologies. Collaborative environment with experienced engineers, product teams, and business stakeholders. Opportunities for professional growth and advancement within a technology-focused organization. Benefits package designed to support physical, mental, emotional, and financial well-being. Supportive workplace culture focused on inclusion, collaboration, and employee development. Work-life balance initiatives and programs supporting personal and professional well-being.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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