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

• 812 years of overall experience in Data Engineering, with at least 34 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.