Snowflake-Lead Data Engineer

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.