Lead Data Engineer

Accountabilities

• • Architect, build, and scale robust data pipelines and infrastructure supporting both AI products and operational applications.
  • Design and maintain data ingestion, transformation, processing, and storage architectures for batch and real-time workloads.
  • Develop scalable systems for vector search, retrieval, and machine-learning data workflows.
  • Build and optimise data models, distributed processing systems, and large-scale query infrastructure.
  • Establish frameworks for data reliability, quality, security, governance, monitoring, and observability.
  • Collaborate with AI and backend engineering teams to support model training, inference, and data-driven product capabilities.
  • Contribute to technical architecture decisions, engineering standards, and the long-term data strategy.
  • Partner with founders and product leadership to translate data capabilities into meaningful product and business decisions.
  • Take ownership as the founding data specialist, helping define the team’s technical culture, standards, processes, and future hiring requirements.

  Requirements

  • 7+ years of professional experience, with significant experience in dedicated data engineering roles and ownership of complex data infrastructure.
  • Strong experience designing and building scalable data pipelines and distributed data systems.
  • Solid experience with relational databases, preferably PostgreSQL; experience with MySQL or comparable technologies is also relevant.
  • Experience working with NoSQL databases and modern vector databases used in AI applications.
  • Strong Python programming skills, including experience with data-processing libraries such as Pandas or Polars.
  • Demonstrated ability to make, communicate, and justify architectural decisions rather than simply implementing predefined solutions.
  • Experience building scalable backend systems and designing robust data models and storage architectures.
  • Strong understanding of data processing performance, scalability, and optimisation.
  • Experience with technologies such as Apache Spark, Apache Airflow, Kafka, Elasticsearch, or OpenSearch is highly desirable.
  • Experience with PostgreSQL, MongoDB, and vector technologies such as Qdrant, Milvus, or pgvector is highly desirable.
  • Strong collaboration and communication skills, with the ability to work effectively with technical and non-technical stakeholders.
  • Experience in AI/ML platforms, event-driven architectures, cloud infrastructure, or high-growth startup environments is a plus.

  Benefits

  • Remote-first working environment from locations across India.
  • Opportunity to take significant ownership as the first senior data engineering hire.
  • High-impact role working at the intersection of data engineering, AI, and intelligent automation.
  • Direct collaboration with AI engineers, backend engineers, product teams, founders, and leadership.
  • Opportunity to shape the data architecture, engineering standards, culture, and future team.
  • Exposure to modern technologies including real-time data pipelines, vector databases, machine-learning infrastructure, and distributed systems.
  • Strong opportunities for professional growth and technical leadership in a rapidly evolving environment.