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Volunteer Opportunity

Senior Data Engineer

NielsenIQ Mexico

Deadline: Not specified

Short Summary

Senior Data Engineer with NielsenIQ. As a Data Software Engineer, you will be responsible for building new data solutions for our rapidly expanding customer base and working with the top data ingestion technologies, working with a team of amazing, diverse-minded, and bright people who make an impact, generate creative & innovative ideas, and take on new perspectives. Responsibilities Own end-to-end data flows from…

Key Details

  • Position / opportunity: Senior Data Engineer
  • Organization: NielsenIQ
  • Country / coverage: Mexico
  • Location: Ciudad de México, DIF, Mexico
  • Work arrangement: On-site
  • Opportunity type: Volunteer opportunities
  • Sector: Education
  • Compensation: Not specified
  • Duration: Not specified
  • Start date: 2026-06-19
  • Application deadline: Not specified

Description

As a Data Software Engineer, you will be responsible for building new data solutions for our rapidly expanding customer base and working with the top data ingestion technologies, working with a team of amazing, diverse-minded, and bright people who make an impact, generate creative & innovative ideas, and take on new perspectives. Responsibilities Own end-to-end data flows from requirements and architecture through implementation and production operations, including Data acquisition, Data set acceptance criteria, and Data Science integration. Design and build scalable batch and real-time data pipelines and lakehouse solutions with a focus on large-scale data processing. Take responsibility to explore technologies to scale up the Data ecosystem to handle rapid Big Data growth. Partner with Data Science to productionize ML/AI workloads and ensure smooth integration into products. Collaborate with cloud, DevOps, application, and client teams to deliver robust, secure, and scalable solutions that solve meaningful business problems. Evaluate and adopt new technologies and patterns to evolve the data ecosystem as scale and complexity grow. 3+ years of hands-on Data Engineering building and operating production-grade Data Systems and Pipelines (Data-Intensive, Distributed Processing, Databases). B.Sc. / M.Sc. in Computer Science, Computer Engineering, or equivalent. Proficiency in Python; working proficiency in Scala. Strong expertise with at least one major cloud provider (AWS, Azure, or GCP). Strong experience with Big Data processing (Spark, DataBricks) and event streaming (Kafka). Experience with orchestration and platform tooling such as Airflow; ability to build maintainable DAGs and operationalize workflows. Strong SQL skills and experience with data storage systems plus at least one of: Data Lake/Lakehouse, columnar DB, or NoSQL systems. Hands-on experience with containers and Kubernetes (Helm is a plus) and modern CI/CD practices. Familiarity with LLM workflow frameworks (LangChain/LangGraph). Proven experience designing, building, and owning production-grade data pipelines (batch and/or streaming), including reliability, backfills, and SLA-driven delivery. Ability to learn new technologies and work in a dynamic fast-paced environment. Result-driven, pragmatic, and innovative. Strong analytical skills, with an open and proactive mindset to investigate, learn, and propose solutions; highly self-driven and self-taught. Strong English communication skills, both written and verbal. Nice to have Experience with Delta Lake and/or Apache Iceberg; ML lifecycle tools such as MLflow. Experience with Pandas/Polars and building data services/APIs (e.g., FastAPI). Experience building LLM-powered agents / chat assistants (RAG, tool/function calling, workflow or multi-agent orchestration), using modern frameworks and platforms. Infrastructure as Code (Terraform/Pulumi/CloudFormation) and cloud security fundamentals (IAM, secrets, encryption). Experience with observability tooling (metrics/logging/tracing) and cost/performance optimization for distributed workloads. Experience building applications with React and Node.js. Our Benefits Flexible working environment Volunteer time off LinkedIn Learning Employee-Assistance-Program (EAP) NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles…

Responsibilities

  • As a Data Software Engineer, you will be responsible for building new data solutions for our rapidly expanding customer base and working with the top data ingestion technologies, working with a team of amazing, diverse-minded, and bright people who make an…
  • Responsibilities Own end-to-end data flows from requirements and architecture through implementation and production operations, including Data acquisition, Data set acceptance criteria, and Data Science integration.
  • Design and build scalable batch and real-time data pipelines and lakehouse solutions with a focus on large-scale data processing.
  • Take responsibility to explore technologies to scale up the Data ecosystem to handle rapid Big Data growth.
  • Partner with Data Science to productionize ML/AI workloads and ensure smooth integration into products.
  • Collaborate with cloud, DevOps, application, and client teams to deliver robust, secure, and scalable solutions that solve meaningful business problems.

Requirements / Eligibility

  • and architecture through implementation and production operations, including Data acquisition, Data set acceptance criteria, and Data Science integration.
  • Design and build scalable batch and real-time data pipelines and lakehouse solutions with a focus on large-scale data processing.
  • Take responsibility to explore technologies to scale up the Data ecosystem to handle rapid Big Data growth.
  • Partner with Data Science to productionize ML/AI workloads and ensure smooth integration into products.
  • Collaborate with cloud, DevOps, application, and client teams to deliver robust, secure, and scalable solutions that solve meaningful business problems.

How To Apply

Use the Apply now button and follow the instructions on the application page.