Data Engineer

Fusemachines is a leading AI strategy, talent, and education services provider, and they are seeking a highly analytical Senior / Mid-Senior Data Engineer to join their team. The role involves building and scaling data systems for a global media measurement company, focusing on data quality assessments and backend data pipelines.

Responsibilities:

• Design, build, and maintain robust, scalable data enrichment pipelines using Apache Spark within an AWS environment
• Utilize AWS S3 as the core target data storage, leveraging AWS Glue and Athena for data cataloging and querying
• Configure, manage, and monitor automated data workflows and pipelines using Airflow
• 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
• Analyze core data to provide actionable insights and proactively suggest pipeline enhancements or new data applications based on findings
• Improve performance, scalability, and cost-efficiency of pipelines, queries, and storage services while resolving bottlenecks
• Act as a technical detective to troubleshoot data delivery and device tracking issues across hybrid server-to-server systems
• Implement monitoring, observability, and debugging mechanisms across all layers (data → API → client SDKs)
• Collaborate with global product, engineering, and data science teams to thoroughly understand logic requirements and provide backend engineering support
• Execute data governance strategies encompassing cataloging, lineage tracking, and schema design working closely with the Data Architect

Requirements:

• 4–8 years of engineering experience, with a strong background in real-world data engineering development on AWS
• Bachelor's degree in Computer Science or related field (advanced studies or exposure to AI/ML is a plus)
• 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
• Strong hands-on experience building data enrichment pipelines using Apache Spark / PySpark
• 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
• Proficiency in Python or Java, combined with a strong understanding of SQL, writing advanced queries, and query optimization
• Skilled in data integration (ETL/ELT) from diverse sources like APIs (REST/gRPC), flat files, databases, and event streaming
• Familiarity with the digital advertising ecosystem (AdTech), audience segmentation, targeting, and media measurement concepts (reach, frequency, attribution, and campaign performance)
• Ability to dig into multi-layered architectures to troubleshoot data mismatches or technical tracking issues, without needing to perform front-end development yourself
• Great problem-solving skills, high attention to detail, and the ability to define and document data engineering processes and data flows
• 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)