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Job Description

Description :


Key Responsibilities :


- Lead the design, development, and optimization of data pipelines and data warehouse solutions on Snowflake.

- Snowflake Types of Tables, Storage Integration, Internal & External Stages, Streams, Tasks, Views, Materialized Views, Time Travel, Fail Safe, Micro partitions, Warehouses, RBAC, COPY Command, File Formats (CSV, JSON and XML), snowpipe, Stored Procedures (SQL or JavaScript, Python).

- Develop and maintain dbt models for data transformation, testing, and documentation.

- dbt : create, run and build a model, Scheduling, Running dependency Models, Macros, Jinga Template (Optional).


- Collaborate with cross-functional teams including data architects, analysts, and business stakeholders to deliver robust data solutions.

- Ensure high standards of data quality, governance, and security across pipelines and platforms.

- Leverage Airflow (or other orchestration tools) to schedule and monitor workflows.

- Integrate data from multiple sources using tools like Fivetran, Qlik Replicate, IDMC (At least one).

- Provide technical leadership, mentoring, and guidance to junior engineers in the team.

- Optimize costs, performance, and scalability of cloud-based data environments.

- Contribute to architectural decisions, code reviews, and best practices.

- CI/CD BitBucket, GitHub (At least one).

- Data Model ENTITY (SUB DIM, DIM, FACTS), Data Vault (HUB, LINK, SAT).

Required Skills & Experience :


- 8- 12 years of overall experience in Data Engineering, with at least 3- 4 years in a lead role.

- Strong hands-on expertise in Snowflake (data modeling, performance tuning, query optimization, security, and cost management).

- Proficiency in dbt (core concepts, macros, testing, documentation, and deployment).

- Solid programming skills in Python (for data processing, automation, and integrations).

- Experience with workflow orchestration tools such as Apache Airflow.

- Exposure to ELT/ETL tools.

- Strong understanding of modern data warehouse architectures, data governance, and cloud-native

environments.

- Excellent problem-solving, communication, and leadership skills.

Good to Have :

- Hands-on experience with Databricks (PySpark, Delta Lake, MLflow).

- Exposure to other cloud platforms (AWS, Azure, or GCP).

- Experience in building CI/CD pipelines for data workflows.

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