Mid-Level Data Engineer

Department/Project Description

The project focuses on developing and modernizing a global data platform that supports data-driven marketing and advertising analytics for a leading international company.
The goal is to improve data quality, scalability, performance, and the reliability of data processing.

Job Description

 - Familiarity with Google Cloud Platform, including BigQuery, Cloud Composer (Airflow), Compute Engine, and Monitoring.
 - If you have hands-on with AWS or Azure, this experiecne is also considerable.
 - Strong proficiency in Python, including experience in building, testing, and deploying ETL/ELT pipelines in production
 - Experience developing data quality frameworks, including automated tests, cross-dataset validations, and anomaly detection across diverse data assets
 - Advanced SQL skills, including data modeling (star/snowflake/3NF), optimization, and writing performant queries tailored to BigQuery’s slot-based execution model
 - Solid working knowledge of DBT, including incremental models, testing, documentation, and advanced features like custom macros
 - Demonstrated ability to optimize BigQuery workloads through partitioning, clustering, materialized views, and cost-aware development practices
 - Experience working with ETL orchestration tools, ideally Cloud Composer or similar frameworks (Kestra, Dagster, etc.).
 - Hands-on experience consuming and managing APIs for data extraction.
 - Exposure to Site Reliability Engineering (SRE) best practices, including ticket triage, incident management, and documenting runbooks/SOPs
 - Familiar with Git and modern software development workflows, including pull requests and code reviews
 - Comfortable working in an agile team environment, with the ability to multitask and reprioritize based on changing project needs
 - English speaking and writing B2+, with the ability to engage technical and non-technical stakeholders alike
 

Job Responsibilities

 - Designing, building, and maintaining scalable, reliable data pipelines using Python,SQL, DBT, and Google Cloud Platform (GCP) services like BigQuery and Cloud Composer.
 - Contributing to the development and evolution of our data quality framework, ensuring robust automated testing and cross-dataset validation across all critical data assets
 - Writing and optimizing advanced SQL to power data models, analytics, and reporting pipelines, with a focus on performance and efficiency within BigQuery
 - Developing and maintaining DBT models, including testing, documentation, incremental loading strategies, and the creation of reusable macros
 - Supporting day-to-day incident and ticket resolution, including root cause analysis and documentation of runbooks/SOPs to drive platform reliability
 - Working with APIs and external data sources to extract, normalize, and integrate new datasets into the platform
 - Participating in agile ceremonies (stand-ups, retros, planning), contributing to sprint goals and helping support the wider team objectives
 - Actively contributing to code reviews, documentation, and peer learning - helping to raise engineering standards and share knowledge within the team
 - Monitoring system health and pipeline performance using GCP-native tools and dashboards, and proactively identifying opportunities for cost or performance optimization