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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # GCP BigQuery+Nifi+Kafa Data Engineer - **Company:** VIRTUES INCORPORATED - **Location:** Irving, TX, United States - **Experience:** Expert - **Salary:** $100,000.0 - $110,000.0 - **Contract:** Permanent contract - **Skills:** Adaptable Database Systems, Agile Methodology, Big Data, BigQuery, Cloud Computing, Cloud Engineering, Cloud Storage, Cluster Analysis, Continuous Integration, Information Engineering, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Mining, Data Warehousing, Database Development, DevOps, Fault Tolerance, Data Flow Control, Python (Programming Language), Routing, Performance Tuning, Scrum Methodology, Query Optimization, Cloud Services, SQL Databases, Data Streaming, Data Processing, Google Cloud, Data Ingestion, Google Data Studio, System Availability, Apache Spark, Data Layers, Data Lakes, Google Bigquery, Real Time Data, Apache Kafka, Apache Nifi, Data Management, Tools for Reporting, Looker Analytics, Data Pipelines - **Published:** July 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e1e2874b216a47f8 ## About the Role · 7+ years of experience in Data Engineering, Data Warehousing, or Big Data platforms. · Mush-Have Skills: Apache NiFi, Apache Kafka. · Strong hands-on experience with Google Cloud Platform (GCP), including BigQuery, Cloud Dataflow, Pub/Sub, and Google Cloud Storage (GCS). · Hands-on experience designing and supporting real-time streaming pipelines using Apache NiFi, Apache Kafka, and Google BigQuery. · Experience using BigQuery Console/Query Editor for data management, performance tuning, and SQL development. · Strong experience designing scalable data models, ETL/ELT pipelines, Data Lake architectures, and enterprise data warehouse solutions. · Experience with batch and streaming data ingestion using GCP services. · Thorough understanding of BigQuery cost structure, including storage, ingestion, and query costs, with experience implementing query optimization, partitioning, clustering, and other cost optimization techniques. · Experience managing large-scale datasets, including temporary/permanent and internal/external BigQuery tables. · Experience developing reporting and visualization solutions using Looker, Looker Studio (Data Studio), and Connected Sheets. · Excellent verbal and written communication skills with the ability to collaborate effectively with technical teams, business stakeholders, senior management, and executive leadership. · Experience working in Agile environments and collaborating with cross-functional teams to deliver enterprise data engineering solutions. Required Technical Skills GCP: BigQuery, Cloud Storage (GCS), Cloud Dataflow, Pub/SubData Engineering: Apache NiFi, Apache Kafka, Python, SQL, Spark, ETL/ELT, Data Modeling, Data Warehousing, Data Lakes, Batch & Streaming Pipelines.Visualization: Looker, Looker Studio, Connected SheetsTools & Practices: CI/CD, DevOps, Agile/Scrum Preferred Qualifications · Google Cloud certifications. · Experience delivering enterprise-scale analytics and cloud data solutions. ## Description We are seeking a highly skilled GCP BigQuery Data Engineer with hands-on experience building cloud-native data platforms and scalable data pipelines. The ideal candidate will have strong expertise in Google BigQuery, Apache NiFi, Apache Kafka, Cloud Dataflow, Pub/Sub, SQL, and ETL/ELT development, with experience designing enterprise data warehouses and Data Lake solutions., · Design, develop, and optimize enterprise data warehouse solutions using Google BigQuery. · Design, develop, and maintain real-time data ingestion pipelines using Apache NiFi to capture, transform, and route streaming data into Apache Kafka topics. · Build scalable event-driven streaming architectures using Apache NiFi, Apache Kafka, and Google BigQueryfor high-throughput, fault-tolerant, and low-latency data processing and analytics. · Configure and optimize Apache NiFi processors for data extraction, transformation, routing, filtering, schema validation, error handling, retry mechanisms, and reliable delivery to Kafka topics. · Develop streaming ingestion solutions enabling Google BigQuery to consume Kafka event streams and transform near real-time data into analytical tables and enterprise reporting datasets. · Design data models and implement ETL/ELT processes to move data from raw to curated and published data layers. · Design, create, and manage large-scale BigQuery datasets, including temporary/permanent and internal/external tables. · Optimize BigQuery workloads through query tuning, partitioning, clustering, and cost optimization techniques to improve performance and reduce cloud costs. · Monitor, troubleshoot, and optimize streaming workloads by tuning NiFi flows, Kafka topics/partitions, and BigQuery streaming ingestion to ensure high availability, data integrity, minimal latency, and cost-efficient processing. · Build scalable Data Lake frameworks and ingestion pipelines using cloud-native GCP technologies. · Develop reporting and visualization solutions using Looker, Looker Studio (Data Studio), Connected Sheets, and other BigQuery reporting tools. · Collaborate with data architects, modelers, developers, DevOps engineers, project managers, and business stakeholders to deliver scalable enterprise analytics solutions and continuous data platform improvements. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Creating a routing app with Google Maps API from scratch](https://www.wearedevelopers.com/videos/831-creating-a-routing-app-with-google-maps-api-from-scratch) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [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) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Got AI ideas but no money? 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