Job
Description
Khushi Baby, a nonprofit organization in India, serves as a technical partner to health departments. Established in 2016 from a Yale University classroom, it has grown into a 90+ member team with offices in Jaipur, Udaipur, Delhi, and Bengaluru. Khushi Baby focuses on digital health solutions, health program strengthening, and R&D. Its flagship platform, the Community Health Integrated Platform (CHIP), supports over 70,000 community health workers across 40,000 villages, reaching 45 million beneficiaries. The platform has identified and monitored 5+ million high-risk individuals, with the Ministry of Health allocating ₹160 crore ($20M) for its scale-up. CHIP has enabled initiatives like Rajasthan's digital health census, TB case finding, vector-borne disease surveillance, labor room monitoring, and immunization drives, co-designed with extensive field input. In R&D, Khushi Baby advances community-level geospatial analysis and individual health diagnostics, including smartphone-based tools and low-literacy models. Programmatically, it focuses on maternal health, child malnutrition, and zero-dose children. Backed by donors like GAVI, Skoll Foundation, and CSR funding, Khushi Baby partners with IITs, AIIMS Jodhpur, JPAL South Asia, MIT, Microsoft Research, WHO, and multiple state governments. Khushi Baby seeks skilled, creative, and driven candidates eager to make a large-scale public health impact by joining its interdisciplinary team in policy, design, development, implementation, and data science. Job Overview We are looking for a Lead Data Engineer to design, build, and optimize scalable data systems for public health analytics. You will define data workflows, layer architecture, and pipelines, ensuring data quality, security, and efficiency while leading a team of engineers. Key Responsibilities Data Architecture & Infrastructure Plan, define, and implement scalable, efficient data architectures, modeling strategies, and workflows to support experimentation, analytics, and product development. Develop and manage real-time and batch processing systems (e.g., Kafka, Flink, RisingWave) to support agile experimentation (A/B testing) and analytics at scale. Strategically anticipate and plan for future data infrastructure needs, ensuring scalability, performance, and cost-effectiveness on cloud platforms (AWS, GCP, Azure). Build and optimize ETL/ELT pipelines for structured and unstructured data, integrating data from diverse public health sources, including state and national health portals. Data Quality, Security & Compliance Lead the design and implementation of robust data quality protocols, embedding quality assurance at every stage—from data collection to ingestion and processing. Proactively shape data collection processes to embed data integrity and standardization from the source. Ensure security, privacy, and compliance with public health data standards such as FHIR, HL7, and ICD-10. Implement robust access control, encryption, and compliance frameworks to meet industry and public health regulatory requirements. Collaboration & Impact Work closely with product, design, and field implementation teams to co-define indicators, monitor product performance, and refine tools based on real-time feedback and experimentation. Translate technical concepts into actionable, business-friendly insights for cross-functional stakeholders.Contribute to defining and iterating KPIs and success metrics through data-driven insights. Team Leadership & Mentorship Manage and mentor data engineers and analysts to develop technical depth, promote a culture of experimentation, and enhance data literacy across the organization. Foster innovation, continuous learning, and the adoption of emerging technologies within the data team. Technical Optimization & Documentation Conduct performance tuning for databases, queries, and data pipelines. Monitor and optimize cloud usage for performance and cost efficiency. Ensure strong documentation of data architectures, workflows, lineage, cataloging, and metadata management processes. Required Qualifications Master’s degree in Computer Science, Data Engineering, or related field. 7+ years of experience in data engineering, with 2+ years in a leadership role. Expertise in SQL, Python, data modeling, and pipeline orchestration (Airflow, Mage AI, etc.). Experience with big data technologies such as Apache Iceberg or Delta Lake. Knowledge of streaming & CDC tools (Kafka, Debezium, Redpanda, etc.). Deep understanding of data engineering best practices including partitioning, indexing, caching, and compression techniques. Strong problem-solving, logical reasoning, communication, and leadership abilities. Hands-on experience with cloud data services (AWS, GCP, Azure). Preferred/Good to Have Experience working on public health data projects with knowledge of key health indicators and metrics. Familiarity with data lakehouse architectures and federated learning frameworks. Strong understanding of public health data interoperability standards (FHIR, HL7, ICD-10). Passion for using data to drive impact in the health and development sectors. Remuneration The remuneration offered will be in the range of 15–25 LPA, depending on the candidate’s experience, skill set, and evaluation based on our internal parameters. Medical Insurance Paid sick leave, paid parental leave and menstrual leave Learning stipend policy A flexible, enabling environment workplace with the opportunity to grow into leadership roles. Opportunities to attend and actively participate in prestigious International conferences and workshops Note : The candidate will be on a probationary period for the first 90 days of the contract s Show more Show less
