Senior Software Engineer (Data Engineer)

Job description

We are seeking a highly skilled Senior Data Engineer to architect, build, secure, and optimize end-to-end data pipelines and enterprise-grade data platforms. The ideal candidate brings deep expertise in Python, Snowflake, modern cloud ecosystems, data warehousing design, and DevSecOps-led automation.  You will mentor junior engineers, drive technical decision-making, and lead solutions that improve data quality, performance, and reliability across the organization.  Data Engineering & Pipelines  • Design, develop, and optimize scalable ELT/ETL pipelines using Python and SQL.  • Build real-time, near-real-time, and batch frameworks using cloud-native services.  • Implement incremental loads, CDC, SCD, schema evolution, and orchestration best practices.  Snowflake Engineering  • Architect and manage Snowflake environments: warehouses, databases, schemas, resource monitors, RBAC, zero-copy clones.  • Implement Snowflake Tasks, Streams, Pipes (Snowpipe) for event-driven data workflows.  • Optimize compute cost and query performance using clustering, micro-partitioning, caching, and warehouse sizing.  Cloud Engineering (AWS | Azure | GCP)  • Build and maintain solutions using cloud-native compute/storage components:   • Azure: Blob Storage, ADLS, Functions, ADF, Purview  Data Warehousing & Modeling  • Design enterprise-grade Data Warehouses, Data Marts, and Semantic Layers.  • Implement Kimball, Data Vault, and modern ELT-first design patterns.  • Work closely with BI/ML teams to operationalize features and analytics models.  DevSecOps & Platform Engineering  • Implement CI/CD pipelines for data engineering code (GitHub Actions / Azure DevOps / GitLab CI).  • Enforce DevSecOps practices:   • secret scanning  • IaC security gates  • dependency scanning  • policy-as-code (OPA/Conftest)  • Build infrastructure using Terraform / Azure Bicep / CloudFormation.  Automated Testing & Data Quality  • Implement data unit testing, schema validation, and contract enforcement:   • pytest  • Great Expectations/dbt tests  • automated data profiling  • Build automated quality dashboards, lineage, and SLA monitoring.  Leadership & Collaboration  • Lead design reviews, code reviews, and data platform roadmap discussions.  • Mentor junior engineers and enforce engineering excellence.  • Partner with Product, Data Science, and Business teams to deliver data-driven solutions.    • 7+ years of professional experience in Data Engineering.  • Strong expertise in Python (pandas, asyncio, OOP, typing, packaging, pytest).  • Hands-on experience with Snowflake at enterprise scale.  • Advanced SQL skills—optimizing complex joins, window functions, CTEs, and performance tuning.  • Strong cloud background: AWS, Azure, or GCP.  • Deep understanding of data warehousing concepts:   • dimensional modeling  • star schemas  • data marts  • CDC/SCD  • modeling for high-volume pipelines  • CI/CD experience with GitHub Actions/Azure DevOps/GitLab CI.  • Experience with IaC tools like Terraform/Bicep.  • Strong understanding of DevSecOps practices:   • secrets management  • IAM design  • vulnerability scanning  • zero trust principles  • Experience with orchestration tools (Airflow/ADF/Prefect/Step Functions).  • Excellent communication and stakeholder management skills.  Preferred / Good-to-Have Skills  • Experience with dbt (models, tests, exposures).  • Streaming platform experience (Kafka, Kinesis, Pub/Sub).  • Experience implementing OpenLineage, Marquez, or other lineage tools.  • Familiarity with Lakehouse architectures (Delta Lake, Iceberg, Hudi).  • Understanding of MLOps concepts and feature stores.  • Knowledge of cost governance and FinOps best practices.  • Prior leadership/mentorship experience.  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 representativ