Data Engineer

Fusemachines is a leading AI strategy, talent, and education services provider. They are seeking a highly analytical Mid-Senior Data Engineer to build and scale data systems for a global media measurement and consumer intelligence 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). Exposure to event data pipelines (impressions, clicks, conversions) and privacy-aware/identity-driven data systems is a plus
• 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)