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
Job DescriptionWe’re looking for a Mid Data Engineer who’s eager to build reliable, secure, and scalable data pipelines while actively adopting AI‑assisted engineering practices. You’ll work with Python, Snowflake, and cloud‑native services to support analytics, reporting, ML workflows, and data‑driven products.
You’ll operate with a DevSecOps mindset, treating security, testing, automation, and AI‑enabled productivity as first‑class citizens. This role expects continuous learning and thoughtful use of tools like GitHub Copilot to improve quality, speed, and maintainability—not to replace engineering judgment
What You’ll Do (Key Responsibilities)
Data Pipelines & ETL/ELT
Develop, schedule, and monitor ELT/ETL pipelines in Python to ingest data from APIs, files, and databases into Snowflake.
Implement transformations (SQL & Python) following modular, reusable patterns.
Leverage AI-assisted tooling (e.g., GitHub Copilot) to accelerate development, improve readability, and reduce boilerplate—while ensuring correctness through reviews and testing
Snowflake Engineering
Build tables, views, stages, and secure data shares.
Tune queries and warehouse configs; manage roles, RBAC, and resource usage.
Use AI assistance to analyze query performance, refactor SQL, and identify optimization opportunities
Cloud & Orchestration
Use cloud services (AWS/GCP/Azure) for storage, compute, and event triggers (e.g., S3/Blob/GCS; Lambda/Functions).
Orchestrate jobs (Airflow/Cloud Composer/Prefect/Azure Data Factory) with proper alerting & SLAs.
Apply AI tools to speed up pipeline scaffolding, DAG generation, and configuration validation
DevSecOps & Reliability
Apply CI/CD for data code (linting, unit/data tests, IaC review gates).
Embed security scanning (dependencies, IaC policies), secrets management, and least‑privilege IAM.
Contribute to observability (logging, metrics, lineage, data quality checks).
Use AI responsibly to assist with test generation, documentation, and failure analysis
Collaboration
Work with Analytics/ML teams to productize models and enable self‑service.
Write clear documentation (readme, runbooks, data dictionaries).
Actively learn and upskill in AI‑driven engineering practices, sharing learnings with the team
3–5 years of experience in data engineering, analytics engineering, or backend engineering (internships/co-ops included).
Programming: Python (pandas, SQLAlchemy/DB APIs, typing, testing with pytest).
SQL & Warehousing: Strong SQL; experience with Snowflake (warehouses, stages, tasks, Snowpipe, RBAC).
Cloud Basics: Familiarity with one major cloud (AWS/Azure/GCP) and storage + compute + IAM fundamentals.
DevSecOps Mindset: Git, CI/CD (GitHub Actions/Azure DevOps/GitLab CI), code reviews, secret handling, dependency scanning.
Data Quality: Exposure to unit tests, data validation (e.g., Great Expectations/dbt tests), and monitoring principles.
AI‑Aware Engineering:
Experience or willingness to adopt GitHub Copilot or similar AI tools
Ability to critically evaluate AI‑generated code and ensure correctness, security, and performance
Strong problem-solving, curiosity, and ability to learn quickly.
Additional Information
Nice-to-Have
Orchestration: Airflow/Prefect/ADF/Composer.
IaC: Terraform/Azure Bicep/CloudFormation; policy-as-code (e.g., OPA/Conftest).
Streaming: Kafka/Kinesis/PubSub; Change Data Capture.
dbt (models, tests, exposures) and semantic layer concepts.
Basic SCD/CDC design patterns; Dimensional modeling (Kimball).
Security: OWASP, least privilege, key rotation, secret stores (AWS Secrets Manager/Azure Key Vault).
Observability: OpenLineage, DataDog/CloudWatch/Log Analytics; SLA/SLO thinking.
Exposure to MLOps (feature stores, model serving) is a plus
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
Skills Required
- 3-5 years of experience in data engineering, analytics engineering, or backend engineering, including internships or co-ops
- Python programming experience, including pandas, SQLAlchemy or database APIs, typing, and pytest testing
- Strong SQL skills and Snowflake experience, including warehouses, stages, tasks, Snowpipe, and RBAC
- Familiarity with AWS, Azure, or GCP, including storage, compute, and IAM fundamentals
- Experience with Git, CI/CD, code reviews, secret handling, and dependency scanning
- Exposure to unit testing, data validation, and monitoring principles
- Experience or willingness to adopt GitHub Copilot or similar AI-assisted engineering tools
- Ability to evaluate AI-generated code for correctness, security, and performance
- Strong problem-solving skills, curiosity, and ability to learn quickly
- Experience with Airflow, Prefect, Azure Data Factory, or Cloud Composer
- Experience with Terraform, Azure Bicep, CloudFormation, OPA, or Conftest
- Experience with Kafka, Kinesis, Pub/Sub, or change data capture
- Experience with dbt, including models, tests, exposures, and semantic layer concepts
- Knowledge of SCD and CDC design patterns or Kimball dimensional modeling
- Knowledge of OWASP, least privilege, key rotation, and cloud secret stores
- Experience with OpenLineage, Datadog, CloudWatch, or Log Analytics and SLA/SLO principles
- Exposure to MLOps, feature stores, or model serving
NielsenIQ Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NielsenIQ and has not been reviewed or approved by NielsenIQ.
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Parental & Family Support — Public materials highlight paid parental leave in the U.S., with additional paid disability time for birth parents. Parental leave is positioned as a standout element of the package.
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Leave & Time Off Breadth — Unlimited or flexible PTO is referenced alongside paid holidays and sick leave, providing broad time-off options in many roles. Actual use can vary by team norms, but the formal breadth is notable.
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Flexible Benefits — Work-from-home and hybrid arrangements are supported in many roles, with remote-friendly practices described across business lines. Flexibility is frequently positioned as a meaningful advantage.
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What We Do
NielsenIQ is the industry leader in global measurement and data analytics, and the most trusted source for retail and consumer intelligence. We deliver the complete truth to retailers, manufacturers, and our partners through our comprehensive data sets and powerful insights. We enable businesses to make critical decisions confidently, accelerating growth and optimizing performance.
Why Work With Us
We empower companies around the world to make bold decisions and transform their business with trusted data, solutions, and insights. Joining NielsenIQ means experiencing countless learning opportunities and gaining numerous pathways for growth.
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