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Bigquery Engineer

  • Bigquery Engineer job type Posted on: Oct 12, 2026
  • Bigquery Engineer experience level Openkyber
  • Bigquery Engineer employment type Atlanta, Georgia
  • Bigquery Engineer salary Salary: $48 per hour
  • Bigquery Engineer employment type Onsite
  • Bigquery Engineer salary Full-time

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Job Title :

Bigquery Engineer

Job Type :

Full-Time

Job Location :

Atlanta Georgia United States

Remote :

No

Jobcon Logo Job Description :

Title: Data Engineer Location: Brooklyn Park, MN | Hybrid Job Type: Contract (6 months) Compensation: $48.00 - $75.00 per hour (W2) Industry: Retail --- About the Role

Our client, a leading enterprise in the retail and consumer services industry, is seeking a Data Engineer to support large-scale data modernization and analytics initiatives. This role will focus on designing and building scalable data platforms, developing high-performance data pipelines, and delivering reliable data solutions that support business intelligence, operational reporting, and advanced analytics. The ideal candidate will have strong experience with cloud-based data engineering, distributed processing frameworks, and DevOps practices, with a focus on Google Cloud Platform (GCP) technologies.

Job Description

As a Data Engineer, you will be responsible for designing, developing, and maintaining robust data solutions that process and transform large volumes of data across enterprise systems. You will work closely with architects, platform engineers, application teams, and business stakeholders to deliver scalable and reliable data products.

Key Responsibilities
  • Design, develop, test, and maintain scalable data pipelines and ETL/ELT workflows for high-volume datasets.
  • Build and optimize distributed data processing solutions using Apache Spark and GCP Dataproc.
  • Develop and maintain analytical datasets and processing workloads in BigQuery.
  • Create reliable batch and streaming data solutions using technologies such as Spark, Hadoop, and Kafka.
  • Design and support data ingestion and transformation pipelines across source systems, data lakes, and analytics platforms.
  • Optimize Spark and BigQuery workloads for performance, scalability, reliability, and cost efficiency.
  • Investigate and resolve production issues through troubleshooting, root-cause analysis, and implementation of long-term solutions.
  • Implement data quality controls, monitoring, logging, alerting, and operational processes.
  • Apply software engineering and DevOps best practices, including source control, CI/CD pipelines, automated testing, and deployment automation.
  • Collaborate with cross-functional teams to translate business requirements into scalable data solutions.
  • Participate in code reviews and contribute to reusable frameworks, standards, and engineering best practices.
Qualifications

Required Qualifications

  • Proven experience in Data Engineering with a strong track record of building and supporting production-grade data pipelines.
  • Hands-on experience designing and developing ETL/ELT solutions.
  • Experience working with Google Cloud Platform (GCP).
  • Strong expertise with BigQuery, including SQL development, performance tuning, and query optimization.
  • Hands-on experience with Apache Spark and distributed data processing frameworks.
  • Experience with GCP Dataproc.
  • Knowledge of Hadoop, Hive, Kafka, and the Apache Spark ecosystem.
  • Strong SQL skills and experience processing and analyzing large datasets.
  • Proficiency in Python and/or Java/Scala.
  • Understanding of data modeling, partitioning strategies, distributed computing concepts, and common data storage formats.
  • Experience with DevOps practices, including Git, CI/CD, automated deployments, and production support.
  • Strong analytical, troubleshooting, and problem-solving skills.

Preferred Qualifications

  • Experience with Looker and/or LookML.
  • Experience working with Google Cloud Storage (GCS), BigLake, Hive, Apache Iceberg, and Parquet.
  • Experience with Infrastructure as Code (IaC) tools such as Terraform.
  • Experience building and supporting Kafka-based streaming pipelines.
  • Experience migrating data platforms or workloads from on-premises Hadoop/Hive environments to GCP.
  • Experience supporting large-scale data lake, cloud migration, or data platform modernization initiatives.
  • Familiarity with data governance, data quality, lineage, metadata management, and related best practices.

Dahl Consulting is proud to offer a comprehensive benefits package to eligible employees that will allow you to choose the best coverage to meet your family's needs.

For details, please review the DAHL Benefits Summary: .

For applications and inquiries,

Jobcon Logo Position Details

Posted:

Oct 12, 2026

Reference Number:

106585-38381

Employment:

Full-time

Salary:

$48 per hour

City:

Atlanta

Job Origin:

CIEPAL_ORGANIC_FEED

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Title: Data Engineer Location: Brooklyn Park, MN | Hybrid Job Type: Contract (6 months) Compensation: $48.00 - $75.00 per hour (W2) Industry: Retail --- About the Role

Our client, a leading enterprise in the retail and consumer services industry, is seeking a Data Engineer to support large-scale data modernization and analytics initiatives. This role will focus on designing and building scalable data platforms, developing high-performance data pipelines, and delivering reliable data solutions that support business intelligence, operational reporting, and advanced analytics. The ideal candidate will have strong experience with cloud-based data engineering, distributed processing frameworks, and DevOps practices, with a focus on Google Cloud Platform (GCP) technologies.

Job Description

As a Data Engineer, you will be responsible for designing, developing, and maintaining robust data solutions that process and transform large volumes of data across enterprise systems. You will work closely with architects, platform engineers, application teams, and business stakeholders to deliver scalable and reliable data products.

Key Responsibilities
  • Design, develop, test, and maintain scalable data pipelines and ETL/ELT workflows for high-volume datasets.
  • Build and optimize distributed data processing solutions using Apache Spark and GCP Dataproc.
  • Develop and maintain analytical datasets and processing workloads in BigQuery.
  • Create reliable batch and streaming data solutions using technologies such as Spark, Hadoop, and Kafka.
  • Design and support data ingestion and transformation pipelines across source systems, data lakes, and analytics platforms.
  • Optimize Spark and BigQuery workloads for performance, scalability, reliability, and cost efficiency.
  • Investigate and resolve production issues through troubleshooting, root-cause analysis, and implementation of long-term solutions.
  • Implement data quality controls, monitoring, logging, alerting, and operational processes.
  • Apply software engineering and DevOps best practices, including source control, CI/CD pipelines, automated testing, and deployment automation.
  • Collaborate with cross-functional teams to translate business requirements into scalable data solutions.
  • Participate in code reviews and contribute to reusable frameworks, standards, and engineering best practices.
Qualifications

Required Qualifications

  • Proven experience in Data Engineering with a strong track record of building and supporting production-grade data pipelines.
  • Hands-on experience designing and developing ETL/ELT solutions.
  • Experience working with Google Cloud Platform (GCP).
  • Strong expertise with BigQuery, including SQL development, performance tuning, and query optimization.
  • Hands-on experience with Apache Spark and distributed data processing frameworks.
  • Experience with GCP Dataproc.
  • Knowledge of Hadoop, Hive, Kafka, and the Apache Spark ecosystem.
  • Strong SQL skills and experience processing and analyzing large datasets.
  • Proficiency in Python and/or Java/Scala.
  • Understanding of data modeling, partitioning strategies, distributed computing concepts, and common data storage formats.
  • Experience with DevOps practices, including Git, CI/CD, automated deployments, and production support.
  • Strong analytical, troubleshooting, and problem-solving skills.

Preferred Qualifications

  • Experience with Looker and/or LookML.
  • Experience working with Google Cloud Storage (GCS), BigLake, Hive, Apache Iceberg, and Parquet.
  • Experience with Infrastructure as Code (IaC) tools such as Terraform.
  • Experience building and supporting Kafka-based streaming pipelines.
  • Experience migrating data platforms or workloads from on-premises Hadoop/Hive environments to GCP.
  • Experience supporting large-scale data lake, cloud migration, or data platform modernization initiatives.
  • Familiarity with data governance, data quality, lineage, metadata management, and related best practices.

Dahl Consulting is proud to offer a comprehensive benefits package to eligible employees that will allow you to choose the best coverage to meet your family's needs.

For details, please review the DAHL Benefits Summary: .

For applications and inquiries,

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