Data Engineer
Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.
Must have skills : Data Build Tool
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Data Engineer (Contract)
Duration: 6 months (with potential to expand capacity) Engagement: Starting with 1 resource
Role Overview
We are seeking an experienced Data Engineer to build and maintain robust, scalable data pipelines on our cloud data platform.
You will work within a modern Medallion architecture, delivering high-quality, well-tested data models and pipelines using dbt, Python, and modern orchestration tools.
Key Responsibilities
Design, build, and maintain dbt data models across Bronze, Silver, and Gold layers of a Medallion architecture
Develop custom dbt macros to enforce modeling standards, reduce code duplication, and improve team productivity
Write production-grade Python code for data ingestion, transformation, and processing workflows
Build, schedule, and monitor data pipelines using orchestration platforms such as Dagster and Astronomer (Apache Airflow)
Implement and manage CI/CD pipelines in GitLab for automated testing, linting, and deployment of dbt projects and Python packages
Collaborate with data architects and analytics engineers to translate business requirements into clean, performant data models and pipelines
Apply data modeling best practices including normalization, slowly changing dimensions, and star/snowflake schema design where appropriate
Leverage AI-assisted development tools such as Amazon Q and Kiro to accelerate pipeline development
Required Skills
Expert-level proficiency in dbt (Core or Cloud), including advanced features such as custom macros, hooks, tests, and packages
Strong Python development skills, including writing modular, testable, and maintainable code for data engineering use cases
Experience building and managing orchestrated workflows using Dagster and/or Astronomer (Apache Airflow), including DAG authoring, sensors, and dependency management
Strong experience with CI/CD pipelines using GitLab, including .gitlab-ci.yml authoring and pipeline optimization
Solid understanding of data modeling principles and experience implementing Medallion (Bronze/Silver/Gold) architecture
Proficiency in SQL and experience working with cloud data warehouses (Snowflake preferred)
Exposure to AWS data services (S3, Glue, Lambda) in a data pipeline context
Familiarity with data quality frameworks such as Great Expectations or dbt tests
Familiarity with AI-assisted development tools (Amazon Q, Kiro, or similar)
Nice to Have (will be mandatory skill set for future adds)
Experience with PySpark for distributed data processing at scale
Bengaluru
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