As a Senior Data Engineer, you will design, develop, and operate data platform components and data-intensive products. You will own significant data engineering work from requirements and design through implementation and production support, partnering with Data Engineering, Data Science, Cloud, DevOps, Application, Product, and client-facing teams to deliver reliable solutions that support business growth. 

This is a hands-on role for an experienced engineer who can solve distributed-data challenges, contribute to sound technical decisions, support other engineers, and help bring emerging technologies into reliable production capabilities. 

Responsibilities 

• Design, develop, and maintain data pipelines, platform components, and data-intensive services. 
• Own data engineering work from requirements and technical design through implementation, deployment, and production support. 
• Contribute to data acquisition strategies, dataset contracts, acceptance criteria, quality standards, lineage, and lifecycle policies. 
• Build scalable batch and real-time data pipelines, lakehouse architectures, and distributed processing solutions for high-volume workloads. 
• Help evolve the data ecosystem to support growth in data volume, velocity, variety, and product complexity. 
• Apply and improve reusable patterns for data ingestion, transformation, storage, orchestration, testing, observability, and delivery. 
• Build production systems that meet expectations for reliability, security, performance, scalability, cost efficiency, and SLA-driven delivery. 
• Contribute to operational practices for monitoring, incident response, failure recovery, replay, backfills, schema evolution, and disaster recovery. 
• Partner with Data Science and ML teams to productionize ML, AI, and LLM-based workloads and integrate them into customer-facing products. 
• Collaborate with Cloud, DevOps, Application, Security, Product, and client teams to deliver robust solutions for business and customer needs. 
• Evaluate technologies and architectural patterns, contribute to technical proofs of concept, and recommend adoption based on measurable value. 
• Document technical decisions, trade-offs, risks, and implementation approaches. 
• Participate in technical reviews and help improve design, code quality, testing, maintainability, and production ownership. 
• Support engineering roadmaps, hiring, and capability development across the data engineering discipline. 

• 6+ years of hands-on software or data engineering experience, including experience designing, building, and operating production-grade data systems. 
• Experience contributing to complex data initiatives across teams. 
• B.Sc. or M.Sc. in Computer Science, Computer Engineering, or an equivalent technical field, or comparable practical experience. 
• Strong understanding of data-intensive and distributed systems, including scalability, reliability, consistency, and performance trade-offs. 
• Strong proficiency in Python and working proficiency in Scala. 
• Strong expertise with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform. 
• Experience with large-scale data processing technologies such as Apache Spark and Databricks. 
• Experience designing and operating event-driven or streaming systems using technologies such as Apache Kafka. 
• Experience with workflow orchestration and platform tooling such as Apache Airflow, including maintainable DAGs and operational business-critical workflows. 
• Advanced SQL skills and strong experience with data modeling, query optimization, and data storage systems. 
• Hands-on experience with at least one of the following: data lake or lakehouse architectures; columnar or analytical databases; or NoSQL or distributed database systems. 
• Experience owning production-grade batch and/or streaming pipelines, including data quality, observability, schema evolution, backfills, recovery procedures, and SLA management. 
• Hands-on experience designing, developing, deploying, and operating production-grade microservices. 
• Hands-on experience with containers, Kubernetes, and modern CI/CD practices; experience with Helm is an advantage. 
• Familiarity with LLM application and workflow frameworks such as LangChain or LangGraph. 
• Ability to evaluate technologies, make sound technical decisions, and communicate trade-offs to engineering and non-engineering stakeholders. 
• Strong collaboration, mentoring, and technical communication skills. 
• Ability to work effectively in a dynamic, fast-paced environment while balancing platform improvements with near-term product delivery. 
• Pragmatic, results-driven approach and commitment to strong engineering fundamentals. 
• Strong written and verbal communication skills in English. 

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 or 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: https://nielseniq.com/global/en/info/niqs-ai-safety-policies/

About NIQ

NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.

For more information, visit NIQ.com

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Our commitment to Diversity, Equity, and Inclusion

At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence.  All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion