Data Engineer

About the Role

Data Engineer

Key Responsibilities

• Develop and implement scalable data pipelines

  using dbt-core, Python, and SQL to support analytics, reporting, and data science initiatives.

• Optimize data models

  in Snowflake to support efficient querying and storage.

• Development and maintenance of our data warehouse

  , ensuring data quality, governance, and performance.

• Collaborate with cross-functional teams

  including data analysts, data architects, data scientists, and business stakeholders to understand data needs and deliver robust solutions.

• Develop and support the best practices SOP

  for version control (Git), CI/CD pipelines, and data pipeline monitoring.

• Task Ownership and Self driven

  , work in a culture of technical excellence and continuous improvement.

• Independently

  execute POC to recommend new tools and technologies to enhance the data platform.
• Provide on-going support for the existing ELT/ETL processes and procedures.
• Other duties as assigned including but not limited to possible reallocation of efforts to other organizations per business need and management request.

Required Qualifications

• Bachelor's degree in computer science or related field (16 years of formal education related to engineering)
• Around 3 years of experience in data engineering with minimum of 1+ years of experience in Snowflake and Python.
• Expert-level proficiency in

  SQL

  for data transformation and automation.
• Experience with

  dbt-core

  for data modeling and transformation.
• Strong hands-on experience in cloud platforms (

  Microsoft Azure)

  and cloud data platforms (

  Snowflake

  ).
• Proficiency with

  Git

  and collaborative development workflows. Familiarity with

  Microsoft

  VSCode

  or similar IDEs. Knowledge of

  Azure DevOps

  or

  Gitlab

  development operations and job scheduling tools.
• Solid understanding of modern

  data warehousing architecture

  , dimensional modeling, ELT/ETL frameworks.
• Excellent communication skills and the ability to translate complex technical concepts to non-technical stakeholders.
• Proven expertise in designing and implementing batch and streaming data pipelines to support near real-time and large-scale data processing needs.

Preferred Qualifications

• Experience working in a cloud-native environment (AWS, Azure, or GCP).
• Familiarity with data governance, security, and compliance standards.
• Prior experience with Apache Kafka (Confluent), DataOps. Live, Atlan.
• Hands-on experience with orchestration tools (e.g., Active Batch, Airflow, Prefect) is a plus.