Data Infrastructure Manager

A premier, globally recognized Life Sciences and DeepTech unicorn engineering an enterprise AI platform for predictive drug discovery and computational immunology. Backed by hundreds of millions of dollars from premier international investors and generating substantial commercial revenue, the company partners directly with the world’s leading pharmaceutical corporations. The R&D environment is characterized by cutting-edge interdisciplinary collaboration between AI researchers, computational biologists, and software engineers solving high-scale distributed data challenges. Located in the Central District directly adjacent to primary train transit, operating on a hybrid model with two days working from home. Role Description- Serving as a Data Engineering Team Lead / Data Infrastructure Manager, reporting directly to the Director of Engineering and leading a core engineering squad of 7–8 engineers, with approximately 20%–30% hands-on technical contribution. Taking complete end-to-end technical ownership over enterprise Big Data, analytics, and data orchestration infrastructures powering distributed AI, ML research, and computational biology teams. Architecting, scaling, and optimizing high-throughput distributed data pipelines and workflows in modern Cloud production environments. Leading the continuous productionization, ingestion, and governance of high-volume patient, molecular, and genomic datasets. Designing and implementing robust orchestration frameworks utilizing modern platforms such as Apache Airflow, Dagster, or equivalent workflow engines. Driving MLOps enablement, bridging the gap between raw data pipelines, deep learning model training clusters, and production inference architectures. Leading agile delivery, roadmap planning, sprint prioritization, technical mentorship, and cross-functional collaboration with global scientific and technical counterparts. Requirements- 10 years of solid, proven software development experience in Backend (BE) or Full-Stack (FS) engineering – Mandatory At least 5 years of proven leadership experience managing dedicated Data Engineering / Data Infrastructure teams – Mandatory At least 4 years of proven direct managerial experience leading teams of at least 8 engineers – Mandatory Proven track record managing multidisciplinary engineering squads (Data Engineering, Backend, Full-Stack, or DevOps) – Mandatory Deep, hands-on architectural and infrastructure knowledge in supporting ML/DL models, distributed AI workflows, and MLOps ecosystems – Mandatory Substantial hands-on experience designing and operating data orchestration engines (e.g., Apache Airflow, Dagster, Prefect, or equivalent) – Mandatory Extensive hands-on expertise in Python, modern Cloud infrastructure (AWS / GCP), and large-scale distributed production data systems – Mandatory B.Sc. degree in Computer Science, Software Engineering, or equivalent technical academic discipline – Mandatory Full-time commitment to working in a hybrid model (3 days on-site in the Central District adjacent to train transit, 2 days remote) – Mandatory Fluent, professional technical communication and management capabilities in English (written and verbal) – Mandatory 155722