Role & 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 boilerplatewhile 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 leastprivilege 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 selfservice.
- Write clear documentation (readme, runbooks, data dictionaries).
- Actively learn and upskill in AIdriven engineering practices,



sharing learnings with the team

Preferred candidate profile

- 3 - 6 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., Excellent Expectations/dbt tests), and monitoring principles.
- AIAware Engineering:
- Experience or willingness to adopt GitHub Copilot or similar AI tools
- Ability to critically evaluate AIgenerated 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

📌 Data Engineer (Chennai)
🏢 NIELSENIQ INDIA
📍 Chennai