Lead Assistant Manager

We are looking for a skilled Data Engineer with strong DBT experience to design, develop, and optimize scalable data transformation pipelines on modern cloud data platforms. The ideal candidate should have hands-on expertise in DBT (Data Build Tool), SQL, cloud data warehouses, and ELT processes.
The role will involve building analytics-ready datasets, implementing data quality frameworks, and collaborating with data architects, analysts, and business stakeholders to deliver reliable and scalable data products.
Required Skills
Core Technical Skills
DBT (Data Build Tool)
Advanced SQL
Data Warehousing Concepts
Data Modeling
ETL / ELT Development
Cloud Platforms
Snowflake
Databricks
Google BigQuery
Azure Synapse Analytics
AWS Redshift
Programming
Python
SQL
Shell Scripting (Preferred)
Data Engineering Tools
Airflow
Azure Data Factory
Databricks Workflows
GitHub / GitLab
Data Quality & Governance
Data Lineage
Data Catalog
Data Validation Frameworks
Metadata Management
Desired Experience
Experience building DBT models on Snowflake or BigQuery.
Experience implementing data validation checks and automated testing through DBT.
Experience creating analytics-ready datasets and semantic models.
Exposure to Medallion Architecture, Lakehouse, and Modern Data Platforms.
Qualifications
Bachelor's degree in Computer Science, Engineering, Information Technology, or related field.
4–8 years of Data Engineering experience.
Minimum 2+ years of hands-on DBT development experience.
Preferred Certifications
SnowPro Certification
Databricks Data Engineer Associate/Professional
Google Professional Data Engineer
Microsoft Azure Data Engineer Associate
Nice-to-Have Skills
DBT Cloud
Jinja Macros
Terraform
Kafka
Spark/PySpark
Data Vault 2.0
AI-assisted development tools (GitHub Copilot, Microsoft Copilot)
Key Responsibilities
Data Engineering & ELT Development
Design and develop ELT pipelines using DBT.
Build reusable, scalable, and maintainable data transformation models.
Develop staging, intermediate, and mart layers following DBT best practices.
Implement modular SQL transformations and macros.
Create and maintain source-to-target mappings.
Data Modeling
Design dimensional models, fact tables, and star schemas.
Build business-friendly semantic layers for reporting and analytics.
Support enterprise data warehouse initiatives.
Data Quality & Testing
Implement DBT tests for:
Uniqueness
Referential integrity
Null validation
Custom business rules
Monitor and improve overall data quality.
Perform root cause analysis for data issues.
Cloud Data Platform Development
Work with cloud platforms such as:
Snowflake
Databricks
BigQuery
Azure Synapse
Redshift
Optimize query performance and manage compute costs.
CI/CD & DevOps
Integrate DBT projects with Git and CI/CD pipelines.
Automate deployments across Development, QA, and Production environments.
Maintain documentation and lineage using DBT documentation features.
Collaboration
Work closely with Data Architects, Analysts, and Business Teams.
Participate in Agile ceremonies and sprint planning.
Translate business requirements into scalable data solutions.

Skills
Python, Git, Kafka, AWS, Azure, CI/CD, Terraform, SQL, Snowflake, Databricks

Work arrangement
Hybrid