Senior Software Engineer at NielsenIQ

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

05 Jul, 26

Salary

0.0

Posted On

06 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Snowflake, SQL, Data Engineering, Cloud Engineering, DevSecOps, Terraform, CI/CD, Data Warehousing, GitHub Copilot, ETL/ELT, AWS, Azure, GCP, Data Modeling, Leadership

Industry

Software Development

Description

Job Description We are seeking a Senior Data Engineer to architect, build, secure, and optimize enterprise‑grade data platforms and end‑to‑end pipelines, while leading AI‑assisted engineering practices across the team. The ideal candidate brings deep expertise in Python, Snowflake, modern cloud ecosystems, data warehousing design, and DevSecOps‑led automation, and actively leverages AI tools (e.g., GitHub Copilot) to drive productivity, quality, and maintainability—without compromising engineering rigor. You will own technical direction, mentor engineers, and set standards for how AI is responsibly embedded into data engineering workflows. Key Responsibilities 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. Leverage AI‑assisted tools (GitHub Copilot, AI code review aids) to: Accelerate pipeline development Refactor legacy code safely Improve SQL and Python quality Generate tests and documentation—validated through reviews Cloud Engineering (AWS | Azure | GCP) Build and maintain production‑grade data solutions using cloud‑native services: Azure: Blob Storage, ADLS, Functions, ADF, Purview (or equivalents in AWS/GCP) Design event‑driven and serverless architectures where appropriate Use AI tooling to accelerate infrastructure design validation and failure analysis 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. Encourage AI usage to: Improve pipeline reliability Reduce mean‑time‑to‑restore (MTTR) Identify operational risks earlier Automated Testing & Data Quality Define quality strategy and enforce shift‑left testing Implement: Data unit testing (pytest) Schema and contract enforcement Great Expectations / dbt tests Automated data profiling Build quality dashboards, lineage, and SLA/SLO monitoring Use AI‑assisted tools to: Identify data anomalies Generate test scenarios Improve coverage and consistency AI Adoption, Learning & Engineering Excellence Champion responsible AI adoption across the data engineering function Lead by example in: Effective GitHub Copilot usage Reviewing and validating AI‑generated code Teaching best practices for AI‑assisted development Continuously evaluate and pilot new AI tools relevant to data engineering Drive a culture of continuous learning, experimentation, and measurable improvement Leadership & Collaboration Lead architecture, design, and code reviews Mentor and guide mid‑ and junior‑level engineers Set engineering standards, patterns, and best practices Partner with Product, Data Science, Security, and Business teams to deliver high‑impact data solutions Influence roadmap decisions with a strong balance of cost, scalability, and reliability Qualifications 6–10 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.Demonstrated AI‑aware engineering mindset, including: GitHub Copilot adoption Critical evaluation of AI outputs Coaching others in safe and effective AI usage 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. AI certifications (GitHub Copilot, Azure AI‑900 / AI‑102) Additional Information Enjoy a flexible and rewarding work environment with peer-to-peer recognition platforms. Recharge and revitalize with help of wellness plans made for you and your family. Plan your future with financial wellness tools. Stay relevant and upskill yourself with career development opportunities. 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://www.nielseniq.com/global/en/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 Want to keep up with our latest updates? Follow us on: LinkedIn | Instagram | Twitter | Facebook 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

Responsibilities

You will architect, build, and optimize enterprise-grade data platforms and pipelines while leading AI-assisted engineering practices. Additionally, you will mentor junior engineers and set technical standards for responsible AI integration within data workflows.