Data Engineer

About Fusemachines Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government. Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI Type: Remote, Full-time About the role: We are seeking a highly analytical Senior / Mid-Senior Data Engineer to join our team as a full-time contractor working in the Indian Timezone . In this role, you will build and scale data systems for a leading global media measurement and consumer intelligence company . This role is tailored for a deeply analytical engineer focused on data quality assessments, backend data pipelines, and pipeline enrichment rather than front-end application creation. You will be responsible for designing, building, and maintaining robust data architectures on AWS. While you will not build front-end applications or mobile SDKs, you must be naturally curious about measurement logic, specifically how user devices are tracked and how hybrid server-to-server architectures operate, as you will help analyze data validity and troubleshoot technical issues across the stack, to power measurement systems that drive advertising effectiveness, audience intelligence, and real-world business decisions at scale . Qualification / Skill Set Requirement: Experience: 4–8 years of engineering experience , with a strong background in real-world data engineering development on AWS . Education: Bachelor’s degree in Computer Science or related field (advanced studies or exposure to AI/ML is a plus). Core Tech Stack (AWS): Proven experience working with an AWS-based data stack, specifically using S3 as the target data storage, Glue for processing, Athena for querying (MPP), and Airflow for workflow orchestration. Big Data Processing : Strong hands-on experience building data enrichment pipelines using Apache Spark / PySpark . Analytical Mindset : Strong analytical background with a verified ability to evaluate data quality, analyze datasets to uncover trends, and suggest potential development applications based on your findings . Programming & SQL : Proficiency in Python or Java, combined with a strong understanding of SQL , writing advanced queries, and query optimization. APIs & Integration : Skilled in data integration (ETL/ELT) from diverse sources like APIs (REST/gRPC), flat files, databases. and event streaming. Media Measurement / Domain Familiarity: Familiarity with the digital advertising ecosystem (AdTech), audience segmentation, targeting, and media measurement concepts (reach, frequency, attribution, and campaign performance). Exposure to event data pipelines (impressions, clicks, conversions) and privacy-aware/identity-driven data systems is a plus. Technical Troubleshooting : Ability to dig into multi-layered architectures to troubleshoot data mismatches or technical tracking issues, without needing to perform front-end development yourself. Preferred / Nice to Have: Experience with streaming systems (Kafka, etc.). Understanding of frontend technologies (React or similar). Understanding of mobile SDK development. Exposure to AI/ML pipelines or LLM ecosystems . Understanding of developer platforms (SDKs, APIs, integration tooling). Professional Attributes : Great problem-solving skills, high attention to detail , and the ability to define and document data engineering processes and data flows . Responsibilities: Pipeline & Enrichment : Design, build, and maintain robust, scalable data enrichment pipelines using Apache Spark within an AWS environment. Data Platform Management : Utilize AWS S3 as the core target data storage, leveraging AWS Glue and Athena for data cataloging and querying. Orchestration : Configure, manage, and monitor automated data workflows and pipelines using Airflow. Data Quality Analytics : Perform deep analytical assessments on pipeline data to evaluate data quality, ensure completeness, and maintain high security standards. Define and enforce data contracts, schemas, and quality standards, implement validation checks and monitoring processes to ensure data accuracy. Insight & Innovation : Analyze core data to provide actionable insights and proactively suggest pipeline enhancements or new data applications based on findings. System Optimization : Improve performance, scalability, and cost-efficiency of pipelines, queries, and storage services while resolving bottlenecks. System Troubleshooting : Act as a technical detective to troubleshoot data delivery and device tracking issues across hybrid server-to-server systems. Observability : Implement monitoring, observability, and debugging mechanisms across all layers (data → API → client SDKs). Cross-Functional Collaboration : Collaborate with global product, engineering, and data science teams to thoroughly understand logic requirements and provide backend engineering support. Data Strategy : Execute data governance strategies encompassing cataloging, lineage tracking, and schema design working closely with the Data Architect. What You’ll Build: High-scale backend platforms that power media measurement, audience intelligence, and data enrichment. Robust AWS-based pipelines that process user tracking and survey data reliably at scale. Automated data quality and analytical systems that validate global-scale consumer intelligence datasets. Why Join this Project: Global Scale : Work on global media and consumer measurement pipelines utilized by leading brands worldwide. Analytical Focus : Engage in deep, logic-driven engineering problems where data analysis and quality are prioritized over UI delivery. Modern Cloud Ecosystem : Gain hands-on ownership of an extensive AWS big data infrastructure optimized for high throughput. Rich Datasets : Direct exposure to uniquely rich datasets spanning retail, consumer behavior, and advertising ecosystems. Innovation & AI : The opportunity to solve complex, high-impact problems at the intersection of data, measurement, and AI. Modern Practices : Collaborate with cross-functional, highly data-driven global teams with a strong focus on innovation and modern engineering practices. Clear Growth : Benefit from a clear growth path toward Staff/Principal Engineer and Platform Leadership roles. Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.