Data Engineer

NielsenIQ; Chennai; On-site; INR 1,500,000 - 2,500,000; Full time

Benefits offered by this job

Flexible working environment

Volunteer time off

LinkedIn Learning

Employee-Assistance-Program (EAP)

Job summary

NielsenIQ is seeking a Mid Data Engineer in Chennai to build reliable data pipelines using Python and Snowflake. You’ll work with cloud services (AWS/Azure/GCP) and orchestration tools, applying DevSecOps practices to ensure secure, scalable analytics platforms and ML workflows.

Collaboration with Analytics/ML teams will enable self-service data products. You will use AI-assisted tooling to speed development while maintaining code quality, security, and observability.

Qualifications

• 3–5 years of experience in data engineering, analytics engineering, or backend engineering.
• Proficiency in Python (pandas, SQLAlchemy/DB APIs, typing, pytest).
• Strong SQL and Snowflake experience (warehouses, stages, tasks, RBAC).
• Familiarity with one major cloud (AWS/Azure/GCP) and storage/compute/IAM basics.
• DevSecOps mindset: Git, CI/CD (GitHub Actions/Azure DevOps/GitLab CI), code reviews, secret handling.
• Experience with data quality tooling and monitoring principles (unit tests, Great Expectations).

Responsibilities

• Develop, schedule, and monitor ELT/ETL pipelines in Python to ingest data into Snowflake.
• Implement transformations (SQL & Python) with modular, reusable patterns.
• Leverage AI-assisted tooling to accelerate development while ensuring correctness via reviews.
• Build and manage Snowflake objects (tables, views, stages, shares) and optimize performance.
• Orchestrate jobs with Airflow/Prefect/Composer and ensure proper alerting and SLAs.
• Apply CI/CD and IaC practices; contribute to observability and security measures.
• Collaborate with Analytics/ML teams to enable self-service and documentation.
• Continuously learn and apply AI-driven engineering practices.

Skills

Python

SQL

Snowflake

ETL/ELT

Cloud

CI/CD

AI tools

Tools

Airflow

Cloud Composer

Prefect

Azure Data Factory

Job description

We'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

Requirements

• 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.

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, N