About the Role :
We are seeking a Senior Data Engineer with a strong blend of engineering rigor and business intuition to own and evolve our data architecture. In this role, you'll lead the development of ETL pipelines from our production MySQL database to a modern data warehouse, enabling analytics and AI across the organization.
You'll work at the intersection of data engineering, analytics engineering, and MLOps-collaborating cross-functionally to transform raw operational data into clean, modeled datasets and semantic layers that power dashboards, ML models, and AI automations.
This is a high-impact, hands-on engineering role with significant ownership and room for innovation.
Key Responsibilities :
Design, build, and maintain robust, scalable ETL pipelines to move data from MySQL production systems to Snowflake and S3-based data lakes.
Write efficient Glue Jobs using Python shell scripts and PySpark .Create reusable ETL frameworks using AWS Glue , SAM templates , and AWS CLI .Build and manage complex Airflow DAGs for orchestration.Modern Data ArchitectureArchitect and evolve a lakehouse architecture leveraging AWS S3, Glue Catalog, and Snowflake.
Implement data modeling best practices in dbt , including raw-to-staged-to-mart transformations that are understandable to the business.Collaborate with analytics and product teams to create reliable, self-serve datasets and semantic layers.APIs & AutomationBuild and consume RESTful APIs to ingest third-party data and expose internal services.
Contribute to building AI-powered data dictionaries , semantic models, and metadata-driven automation workflows.Data Governance & SecurityImplement data masking , row-level , and column-level security policies in Snowflake.
Ensure best practices for access control and secure data movement across environments.AI / ML InfrastructureSupport the creation of ML pipelines and contribute to the MLOps infrastructure .
Help productionize and monitor AI and automation initiatives.Requirements :
5+ years of experience in data engineering roles.Deep knowledge of Python , SQL , and distributed data processing (e.g., PySpark ).Strong experience with AWS services , especially S3 , Glue , CloudWatch , and IAM .Proven experience with Snowflake -including security, optimization, and data modeling.Hands-on with dbt for analytics engineering and data modeling.Proficient in Airflow for workflow orchestration.Experience working with APIs (both consumption and development).Understanding of modern data architectures (data lakes, lakehouses, data mesh).Familiar with MLOps concepts , model lifecycle management, and AI-driven data tools.Experience with AWS CLI and infrastructure-as-code using SAM templates or CloudFormation.Passion for improving data accessibility, building semantic layers, and enabling business users.Bonus Points :
Experience with CI / CD for data pipelines .Exposure to AI / LLM-assisted tools for metadata management and documentation.Prior work on real-time data streaming or event-driven architectures .Why Join Us?
A data-first culture with strong executive support for AI, automation, and analytics.Opportunity to work on high-impact systems with end-to-end ownership.Innovative environment with room to explore new technologies and best practices.Competitive compensation, flexible remote work, and a collaborative team.Ready to build the future of data with us?
Apply today and be part of a team pushing the boundaries of modern data engineering.
