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