We are seeking an experienced Data Engineer with 5+ years of hands-on expertise across the modern data stack to design, build, and maintain high-performance ELT pipelines. In this role, you will be the core technical owner of our analytics foundation—leveraging Snowflake , dbt , hosted on cloud infrastructure ( AWS, Azure, or GCP ). You will partner closely with data analysts, analytics engineers, and business stakeholders to ensure our data models are scalable, reliable, and governed.

Key Responsibilities

- Data Pipeline Architecture & ELT: Design, develop, and optimize robust, idempotent ELT data pipelines moving data from diverse operational sources into Snowflake.
- Transformation & Modeling (dbt): Architect modular, production-grade dbt projects. Implement dimension and fact tables (Kimball/medallion architecture), write reusable Jinja macros, and establish automated testing (data freshness, uniqueness, referential integrity).
- Workflow Orchestration (Airflow): Build, monitor, and maintain production Airflow DAGs with proper error handling, SLA alerts, retries, and dependency management.
- Snowflak & Optimization: Configure and optimize virtual warehouses, storage, clustering keys, zero-copy cloning, and Time Travel. Enforce cost-governance practices and resource monitors.
- Cloud Infrastructure & Ingestion: Integrate cloud storage services (e.g., AWS S3, Azure Blob/ADLS, or Google Cloud Storage) with Snowflake using Snowpipe, external stages, and event-driven architectures.
- Data Quality & Governance: Establish schema migrations, data testing frameworks, and role-based access control (RBAC), object tagging, and data masking policies.




- CI/CD & DataOps: Implement version control best practices, automated deployment pipelines (GitHub Actions, GitLab CI, or Bitbucket Pipelines), and environment management for data assets.

Qualifications & Requirements Must-Have:
- Experience: 5+ years of dedicated qualified experience in data engineering, data warehousing, or business intelligence backend systems.
- Snowflake: Deep expertise in Snowflake architecture, query profiling, warehouse right-sizing, Snowpipe, and security implementations (RBAC, row-level security).
- dbt (Data Build Tool): Proven track record modeling complex datasets in dbt Core or dbt Cloud, using incremental models, snapshots, tests, documentation, and Jinja/SQL macros.
- Any Major Cloud Platform: Solid working experience with at least one major cloud provider ( AWS, Azure, or GCP ), particularly with IAM roles, object storage, and serverless compute.
- Programming & SQL: Mastery of advanced SQL (window functions, CTEs, query plan optimization) and intermediate-to-advanced proficiency in Python for data processing and automation.
- Version Control & CI/CD: Hands-on experience with Git branching workflows and automated testing/deployment pipelines.

Nice-to-Have:
- Experience with streaming technologies (Apache Kafka, AWS Kinesis) or Snowflake Streaming Snowpipe.
- Exposure to Snowflake Dynamic Tables, Streams & Tasks, or Snowpark.
- Relevant certifications: SnowPro Core / Advanced , dbt Certified Developer , or Associate/Professional Cloud Architect/Data Engineer (AWS/Azure/GCP).
- Familiarity with data observability tools (e.g., Monte Carlo, Datafold, Elementary).

📌 Data Engineer (Chennai)
🏢 RandomTrees
📍 Chennai