Principal Software Engineer

Job description

We are seeking a highly skilled **Principal Software Engineer – Data Engineering** to design, develop, and optimize scalable data platforms and pipelines. The ideal candidate will have strong expertise in **Python, SQL, Snowflake (or equivalent cloud data warehouse), cloud technologies, CI/CD practices, and AI-enabled data solutions**. This role requires technical leadership, architecture ownership, mentoring capabilities, and hands-on engineering excellence. Key Responsibilities Data Engineering & Platform Development •  Design and implement scalable, reliable, and secure data pipelines. • Develop ETL/ELT solutions for processing large-scale structured and unstructured datasets. • Build data ingestion frameworks supporting batch and near real-time processing. • Design and optimize data models for analytics, reporting, and machine learning workloads. • Ensure high data quality, governance, observability, and reliability across data platforms. Cloud & Data Warehouse • Architect and maintain cloud-native data solutions on AWS or Azure or GCP. • Develop and optimize solutions using **Snowflake** or equivalent cloud data warehouse technologies. • Implement data lake and lakehouse architectures. • Optimize storage costs, compute utilization, and query performance. Software Engineering Excellence • Develop production-grade applications and frameworks using Python. • Perform code reviews and establish engineering best practices. • Drive architectural decisions and technology selection. • Ensure adherence to security, scalability, and reliability standards. CI/CD & DevOps • Design and implement CI/CD pipelines for data and application deployments. • Automate build, test, deployment, and monitoring processes. • Manage Infrastructure as Code (IaC) using Terraform, CloudFormation, or similar tools. • Improve deployment reliability and observability. AI & Advanced Analytics • Enable AI/ML workflows by building robust feature and training data pipelines. • Work with AI teams to operationalize machine learning models. • Explore Generative AI, LLM integrations, vector databases, and AI-driven automation solutions. • Support model monitoring, governance, and deployment pipelines. Leadership & Mentoring • Provide technical leadership across multiple engineering teams. • Mentor senior engineers and establish engineering standards. • Collaborate with architects, product managers, and business stakeholders. • Drive innovation and strategic technology initiatives.   10+ years of experience in Data Engineering. • Strong expertise in **Python programming**. • Advanced SQL development and performance tuning skills. • Experience with **Snowflake** or equivalent cloud data warehouse technologies. • Strong understanding of data modeling, ETL/ELT frameworks, and data governance. • Experience with cloud platforms: •   Microsoft Azure (Preferred) •   AWS •  Google Cloud Platform DevOps & CI/CD • Experience with: Azure DevOps, GitHub Actions, Jenkins, GitLab CI/CD, Knowledge of Docker and Kubernetes., Infrastructure as Code experience.   AI & Modern Data Technologies Exposure to: •   Machine Learning pipelines •   Generative AI concepts •   LLM integrations •   Vector databases •   RAG architectures •   Prompt engineering basics   Preferred Qualifications: • Experience building enterprise-scale data platforms. • Knowledge of Spark, Databricks, Kafka, or streaming technologies. • Understanding of Data Mesh and modern data architecture principles. • Experience in observability and monitoring tools. • Snowflake, Azure, AWS, or GCP certifications are a plus.   Desired Competencies • Technical leadership • Solution architecture • Strategic thinking • Stakeholder management • Problem-solving • Mentoring and coaching • Strong communication skills • Agile development practices   Success Metrics • Reliable and scalable data platform delivery. • Reduced pipeline failures and improved SLA compliance. • Improved data quality and governance. • Faster deployment cycles through CI/CD automation. • Successful adoption of AI-enabled data solutions. • Increased engineering productivity and platform efficiency. 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-safe