GCP Data Engineer - Mid Level

About the Role

In this role, you'll build, run, and improve data pipelines, data models, and analytics-ready datasets on Google Cloud Platform. You'll partner with senior engineers, architects, analytics teams, and business users to make data accessible, secure, trustworthy, and easy to use.

Your work will power reporting, analytics, and new natural-language insight experiences, built on BigQuery, Looker, and AI-driven data workflows.

Key Responsibilities

• Build, maintain, test, and monitor data pipelines and data products on GCP.
• Develop batch and near-real-time ETL/ELT workflows using proven engineering patterns and GCP services.
• Write efficient SQL and Python to transform, validate, and prepare data for analytics, reporting, and AI use cases.
• Help design and maintain dimensional data models, including Star and Snowflake schemas.
• Load, transform, optimize, and manage enterprise datasets in BigQuery.
• Connect BigQuery to the Looker semantic layer to ensure consistent business definitions and governed metrics.
• Prepare datasets, metadata, documentation, and retrieval components for conversational AI and natural-language analytics.
• Strengthen data quality, lineage, observability, and pipeline monitoring.
• Contribute to building and testing AI-driven workflows such as RAG, Text-to-SQL, AI agents, and LLM-based analytics.
• Optimize BigQuery for query performance, partitioning, clustering, cost efficiency, and access control.
• Contribute to version control, automated testing, deployments, and CI/CD.
• Work closely with Data Architects, Senior Data Engineers, BI Developers, Data Scientists, and business stakeholders.

Agile Delivery & Collaboration

• Take part in Agile ceremonies (sprint planning, stand-ups, backlog refinement, reviews, and retrospectives).
• Break user stories down into technical tasks and deliver iteratively each sprint.
• Join data and AI quality testing to catch inaccurate outputs, inconsistent metrics, hallucinations, and data-access risks.
• Document technical designs, pipeline logic, transformations, and operational procedures.
• Apply best practices in code review, testing, releases, production support, data governance, and security.

Requirements

• 3 to 5 years of experience in data engineering, analytics engineering, software engineering, or a related technical field.
• At least 2 years of hands-on GCP experience building or supporting data solutions.
• Solid command of BigQuery, SQL, Python, pipeline and ETL/ELT concepts, and GCP services such as Dataflow, Cloud Composer, Pub/Sub, Cloud Storage, Cloud Functions, or Cloud Run.
• Experience with data models and pipelines for analytics or reporting.
• Understanding of data warehousing, dimensional modeling, data quality checks, and performance tuning.
• Working knowledge of Looker (semantic modeling, governed metrics, explores, or reporting datasets).
• Some practical or project exposure to AI/ML pipelines, RAG, Text-to-SQL, AI agents, LLMs, or conversational analytics.
• Familiarity with version control, deployment workflows, and CI/CD.
• Strong analytical skills, attention to detail, and a collaborative mindset.
• Clear written and spoken communication.

Nice to Have

• Highly valued: experience with Sell-Out and/or Supply Chain data (processes, data flows, metrics, or reporting).
• Experience with other BI tools such as Power BI, Tableau, or Looker Studio.
• Familiarity with Terraform, Docker, Kubernetes, APIs, or Cloud Run.
• Exposure to AI evaluation, data governance, metadata management, or secure data-access patterns.
• Experience designing Skills, AI agents, and conversational workflows that turn business questions into clear, accurate insights.
• Google Cloud Associate Cloud Engineer or Professional Data Engineer certification, or working toward one.