GCP Data Engineer

As a GCP Data Engineer, you will design and build cloud-based data products, automated data pipelines, and analytical data marts that support business intelligence, analytics, and AI-driven initiatives. You will leverage Google Cloud Platform, Python, SQL, and API integrations to deliver scalable, reliable, and well-documented data solutions that support enterprise reporting and decision-making., * Design and develop scalable data marts and analytical data products within Google Cloud Platform.

• Build automated pipelines that ingest, transform, and validate data from third-party APIs and enterprise systems.
• Develop Python-based solutions for API integrations, data extraction, transformation, and automation.
• Create and optimize complex SQL queries to support data integration, reporting, and analytics.
• Design data models that support business intelligence, reporting, analytics, and AI use cases.
• Integrate marketing, business, and external data sources into curated analytical datasets.
• Implement data quality controls, monitoring, logging, reconciliation, and exception handling processes.
• Maintain and enhance existing digital marketing data marts and reporting datasets.
• Create comprehensive technical documentation covering architecture, data flows, business rules, and support procedures.
• Collaborate with analytics, engineering, security, architecture, and production support teams to deliver production-ready solutions.

• Professional experience in Data Engineering, Analytics Engineering, Software Engineering, or a related technical discipline.
• Advanced SQL expertise with the ability to write, optimize, and troubleshoot complex queries.
• Strong Python development experience for API integration, automation, and data transformation.
• Hands-on experience building ETL/ELT pipelines and analytical data models.
• Experience integrating and processing data from REST APIs and third-party platforms.
• Strong experience working with Google Cloud Platform and cloud-based data services.
• Knowledge of dimensional modeling, data warehousing concepts, data grain, and reusable analytical assets.
• Experience implementing data quality validation, monitoring, logging, and exception management solutions.
• Strong technical documentation, communication, and stakeholder collaboration skills.
• Experience with dbt, dlt, DuckDB, Adobe Analytics, BI platforms (Tableau, Looker, Power BI), AI/LLM technologies, GitHub, CI/CD, and highly regulated industries is preferred.