> Markdown version of [/jobs/ext/3550323-data-engineer-gcp](https://www.wearedevelopers.com/jobs/ext/3550323-data-engineer-gcp). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # DATA Engineer - GCP - **Company:** Krest Global Solutions LLC - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Salary:** $120,640.0 - $124,800.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, BigQuery, Cloud Storage, Cluster Analysis, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Data Flow Control, Data Intelligence, Python (Programming Language), Performance Tuning, Cloudera, SQL Databases, Google Cloud, Snowflake, Apache Spark, Containerization, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Apache Kafka, Machine Learning Operations, Terraform, Data Pipelines, Automation Anywhere, Docker, Jenkins, Databricks - **Published:** September 30, 2026 - **Apply:** https://www.juju.com/job/16_ee3360017 ## About the Role We are looking for a highly capable Data Engineer to design, build, and maintain reliable, scalable data pipelines and infrastructure on Google Cloud Platform (GCP).. The ideal candidate is proficient with BigQuery, Dataflow, and has experience integrating modern tools like Vertex AI and Gemini models for intelligent data workflows., * Strong experience with GCP services: BigQuery, Dataflow, Pub/Sub, Cloud Storage * Expertise in SQL, Python, and ETL/ELT development * Knowledge of Infrastructure as Code tools (e.g., Terraform) * Familiarity with CI/CD tools: Google Cloud Build, Jenkins * Understanding of data modeling, partitioning, clustering, and materialized views * Working knowledge of data quality frameworks and governance principles * Experience with Vertex AI, or a strong interest in ML/AI workflows on GCP * Apache Spark, Kafka, Apache Airflow * DBT or Dataform for transformations * Docker, Kubernetes for Containerization * Snowflake, Databricks or other modern platforms * Soft Skills: * Strong communication and collaboration skills * Ability to manage priorities and work independently * Analytical thinking and problem-solving mindset, * Google Cloud certifications (e.g., Professional Data Engineer, ML Engineer) * Domain experience in energy, utilities, or industrial sectors, * Education: Bachelor's degree in computer science, Engineering, Statistics, or related technical field (or equivalent experience) * Professional Experience: * 8-10 years of experience in data engineering * Proven hands-on experience with GCP and modern data architecture ## Description * Data Pipeline Development: Build scalable batch and real-time pipelines using Dataflow, Pub/Sub, Cloud Composer, and Dataproc. * Data Warehousing: Design and optimize analytical models in BigQuery, implementing best practices in schema design and performance tuning. * Infrastructure & CI/CD: Deploy data infrastructure with Terraform; create and manage CI/CD pipelines for workflow automation. * Data Quality & Governance: Implement validation checks, ensure data integrity, and enforce security and governance practices. * AI/ML Integration: Collaborate with data scientists to support Vertex AI workflows and explore the use of Gemini models (via BigQuery ML or Vertex AI APIs) for advanced data transformation. * Cross-Team Collaboration: Work with analysts, scientists, and business stakeholders to deliver impactful data solution. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [GitLab CI pipelines for a whole company](https://www.wearedevelopers.com/videos/143-gitlab-ci-pipelines-for-a-whole-company) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [The Cloud is Calling: Answer with In-Demand Skills](https://www.wearedevelopers.com/videos/945-the-cloud-is-calling-answer-with-in-demand-skills) - [Our GitOps approach for deploying an Identity Provider and an API Gateway in a SaaS company](https://www.wearedevelopers.com/videos/776-our-gitops-approach-for-deploying-an-identity-provider-and-an-api-gateway-in-a-saas-company) ## Related Articles - [Got AI ideas but no money? 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