Senior Data Engineer (Data Platform)

Overview

As a Senior Data Engineer at Teya, you will shape and scale the data platform that powers analytics and AI capabilities across the organization. You’ll own the data infrastructure, ELT pipelines, and governance, enabling reliable decision-making for cross-functional teams. You’ll contribute to platform architecture, data product thinking, and engineering best practices. This role offers impact by simplifying data access while upholding quality and governance, in a fast-moving, collaborative environment.

Responsibilities

• Designing and evolving the data infrastructure architecture
• Maintaining the existing data platform
• Ensuring reliability of data ingestion pipelines
• Sharing learnings with the Data Engineering team and beyond
• Enforcing data governance with streamlined processes
• Improving data reliability, quality, and observability across datasets
• Building and refining data models for large datasets with simple lifecycle
• Designing and optimizing ETL/ELT pipelines for scalable analytics
• Collaborating with analysts and engineering teams to deploy production data solutions
• Participating in on-call rotations and incident response improvements
• Contributing to technical discussions, code reviews, and maintainable designs
• Creating and maintaining technical documentation and runbooks

Key requirements

• 5+ years in Data Engineering or Data Platform/Software Engineering with Data
• Strong SQL expertise for complex analytical queries
• Proficiency in Python and/or Java
• Experience with Snowflake or Apache Iceberg
• Practical knowledge of Docker and Kubernetes
• Knowledge of streaming tools (Kafka, Kafka Connect, Flink)
• Experience with ETL/ELT tooling (dbt, Airflow, Airbyte, dlt)
• Hands-on provisioning and managing cloud infrastructure with Terraform
• Experience with CI/CD pipelines, automated testing and Git workflows
• Familiarity with observability (logging, metrics, alerting, production troubleshooting)
• Strong software engineering principles and best practices
• Experience contributing to or leading data warehouse architecture or redesign initiatives
• Ability to collaborate with technical and non-technical stakeholders
• Strong experience with data warehousing, dimensional modeling, and data architecture

• Collaborative mindset
• Effective communication
• Problem-solving and initiative
• SQL
• Python
• Java

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