Senior Data Engineer

As part of our strategic evolution, we are transforming our established platform into a next-generation, AI-first, agentic ecosystem. This transformation requires a strong, scalable, and reliable data foundation to support intelligent applications, AI agents, advanced analytics, and data-driven business capabilities.
We are looking for an innovative and experienced Data Engineer to join our team and play a key role in building this foundation.
The Data Engineer will be responsible for designing, developing, and maintaining scalable data architectures, ETL pipelines, and infrastructure to transform raw enterprise business data into clean, secure, reliable, and highly available data assets. This data infrastructure will power AI agents, machine learning models, and automated agentic workflows, enabling the next generation of intelligent business solutions across our ecosystem.

Responsibilities:

• Data Warehouse Architecture: Design and implement a modern, scalable, multi-tenant Data Warehouse architecture leveraging cloud-based data platforms such as Google BigQuery and Snowflake.
• Data Pipelines & Orchestration: Design and implement ETL/ELT pipelines, manage dbt transformations of complex B2B/ERP data structures, and optimize incremental data loading. Orchestrate workflows, including scheduling, monitoring, and failure recovery.
• Data Quality & Auditing: Implement automated data quality testing, anomaly detection, and data auditing mechanisms, including field-level historical change tracking, within the data pipelines.
• Data Governance & Security: Define and implement data access controls, tenant isolation, and data privacy policies. Establish and maintain data dictionaries, metadata, data lineage, and documentation standards.
• Performance & Cost Optimization: Optimize query performance, design effective partitioning and clustering strategies, and manage cloud resource utilization and costs to ensure scalability and efficiency.
• CI/CD & Deployment: Manage data models using Git version control and implement automated testing, validation, and deployment workflows for dbt projects using CI/CD tools such as GitHub Actions.
• Cross-Functional Collaboration: Partner with backend engineers, data scientists, and data product managers to define data requirements, establish data contracts, and deliver reliable, scalable data products and infrastructure that support analytics, machine learning, and AI applications.

Requirements:

• Experience: 6+ years of hands-on experience as a Data Engineer in high-scale, event-driven environments. Strong proficiency in SQL and Python, with a solid background in Git, version control, and CI/CD workflows for data deployment.
• dbt Mastery: Demonstrated expertise in building and maintaining dbt transformation workflows, including staging raw data, transforming it into clean, integrated Silver-layer datasets and business-ready Gold-layer datasets, and implementing advanced incremental loading strategies.
• Database Design Mastery: Demonstrated expertise in designing and managing complex database schemas, tables, relationships, constraints, indexes, and data types.
• Performance Optimization Expertise: Strong understanding of query execution, distributed data processing, partitioning, clustering, and cloud data warehouse cost optimization.