We are looking for an experienced Data Engineer to join our Bengaluru team and help scale the development of our internal data products. In this role, you will build data-driven applications that empower teams across the company, including go-to-market, engineering, and product. Responsibilities: Design and implement core components of our data pipelines and services in Go and Scala, with a focus on scalability, performance, and long-term maintainability. Partner closely with engineers, analysts, and product stakeholders to design solutions for strategic initiatives, business-critical data products, and agentic AI architectures. Contribute to the evolution of our data platform architecture, improving scalability, reliability, observability, and data correctness across the stack. Build and curate high-quality, well-modeled, and richly contextual datasets that power internal products, predictive analytics, and LLM-enabled features at company scale. Develop a deep understanding of the company’s data ecosystem , including source systems, tooling, and data flows , and collaborate closely with the data engineering teams in Austin, Lisbon, and London to improve data ingestion, quality, and governance. Lead by example through design reviews, knowledge sharing, and mentorship, helping raise the technical bar, data engineering practices, and AI implementation standards across the organization. Requirements: B.S. or M.S. in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience. 5+ years of professional experience in data engineering, software engineering, or related roles, building and operating production data systems. Strong programming expertise in Go, Python, or JVM-based languages, with experience writing high-quality, production-grade services and pipelines. Deep knowledge of SQL and hands-on experience designing data models and working with relational, analytical, or vector databases (e.g., PostgreSQL, MySQL, ClickHouse). Experience building scalable, reliable, and observable data pipelines, with an understanding of performance, data quality, and operational best practices. Proven problem-solving and communication skills, with a track record of driving projects in ambiguous environments and partnering effectively with cross-functional teams. Nice to Have: Familiarity with container-based deployments such as Docker & Kubernetes. Familiarity with Google Cloud Platform, foundational Large Language Model (LLM) orchestration frameworks, or something similar.
Large Language Model (LLM) orchestration frameworks