Senior Data Engineer (Remote)

Dahl Consulting; Brooklyn Park (MN); Hybrid; USD 92,000 - 156,000; Full time

Job summary

Dahl Consulting is seeking a Senior Data Engineer for a national retailer with a sophisticated tech organization. This remote contract role focuses on building and operating large-scale data pipelines across Hadoop and GCP, delivering reliable batch and streaming workloads.

The ideal candidate will own data engineering work from design through production, collaborate with engineers, analysts, and data scientists, and optimize pipelines for performance, cost, and reliability in a fast-paced

Qualifications

• 5+ years of hands-on data engineering or related software engineering experience building large-scale data systems.
• Strong proficiency in Python and SQL.
• Hands-on experience with Apache Spark and distributed data processing.
• Experience with the Hadoop ecosystem, including HDFS and Hive.
• Hands-on experience with Kafka or another event-streaming technology.
• Experience building and supporting ETL/ELT and data integration pipelines.
• Experience with Google Cloud Platform data services, particularly BigQuery.
• Experience optimizing data pipelines for performance, scalability, reliability, and cost.
• Experience supporting production data systems, including monitoring, logging, incident troubleshooting, and operational support.
• Working knowledge of Git, CI/CD, automated testing, and Agile engineering practices.
• Ability to independently own data engineering work from design through production deployment and ongoing support.
• Strong communication and collaboration skills with the ability to work effectively across technical and business teams.

Responsibilities

• Design, build, test, deploy, and support scalable batch and streaming data pipelines using Python, SQL, Apache Spark, Kafka, and related technologies.
• Support the modernization and migration of data pipelines and datasets from on-premises Hadoop environments to Google Cloud Platform (GCP), including BigQuery and other cloud-native services.
• Build and optimize ETL/ELT workflows for large-scale, high-volume datasets with an emphasis on performance, scalability, reliability, and cost efficiency.
• Develop trusted datasets supporting analytics, fraud investigation, reporting, and machine learning use cases.
• Implement appropriate data quality checks, testing, and validation processes.
• Troubleshoot production data issues, conduct root-cause analysis, and implement durable solutions to prevent recurring failures.
• Implement and maintain monitoring, alerting, automation, and CI/CD practices for production data pipelines.
• Work across both legacy Hadoop and modern GCP environments during ongoing platform modernization.
• Partner with engineers, analysts, data scientists, product teams, and other stakeholders to understand data requirements and deliver effective solutions.
• Identify technical risks, data quality concerns, performance issues, and opportunities to improve platform reliability.
• Contribute to technical design, code reviews, engineering standards, documentation, and knowledge transfer.

Skills

Python

SQL

Apache Spark

Kafka

Hadoop

Hive

BigQuery

Dataproc

CI/CD

Git

Airflow

Data pipelines

Tools

Hadoop ecosystem

Hive

BigQuery

Dataproc

Dataflow

Pub/Sub

Cloud Storage

Airflow

Cloud Composer

Oozie

Spark Streaming

Flink

Job description

Senior Data Engineer

Location: Remote

Job Type: Contract (6-12 Months)

Compensation: $67.66 - $112.78/hr

Industry: Retail

About the Role

We are hiring a Senior Data Engineer on behalf of our client, a large national retailer with a sophisticated technology organization supporting high-volume digital and enterprise operations. This is a hands-on, execution-focused opportunity supporting a Data Services Engineering team within a fraud and abuse technology environment. The Senior Data Engineer will build, modernize, and operate large-scale data pipelines and platforms across Hadoop and Google Cloud Platform (GCP). The ideal candidate is a strong individual contributor who can quickly become productive within an established architecture, independently own data engineering work from design through production support, and deliver reliable solutions for high-volume batch and streaming workloads.

Job Description

Design, build, test, deploy, and support scalable batch and streaming data pipelines using Python, SQL, Apache Spark, Kafka, and related technologies. Support the modernization and migration of data pipelines and datasets from on-premises Hadoop environments to Google Cloud Platform (GCP), including BigQuery and other cloud-native services. Build and optimize ETL/ELT workflows for large-scale, high-volume datasets with an emphasis on performance, scalability, reliability, and cost efficiency. Develop trusted datasets supporting analytics, fraud investigation, reporting, and machine learning use cases. Implement appropriate data quality checks, testing, and validation processes. Troubleshoot production data issues, conduct root-cause analysis, and implement durable solutions to prevent recurring failures. Implement and maintain monitoring, alerting, automation, and CI/CD practices for production data pipelines. Work across both legacy Hadoop and modern GCP environments during ongoing platform modernization. Partner with engineers, analysts, data scientists, product teams, and other stakeholders to understand data requirements and deliver effective solutions. Identify technical risks, data quality concerns, performance issues, and opportunities to improve platform reliability. Contribute to technical design, code reviews, engineering standards, documentation, and knowledge transfer.

Qualifications

• 5+ years of hands-on data engineering or related software engineering experience building large-scale data systems.
• Strong proficiency in Python and SQL.
• Hands-on experience with Apache Spark and distributed data processing.
• Experience with the Hadoop ecosystem, including HDFS and Hive.
• Hands-on experience with Kafka or another event-streaming technology.
• Experience building and supporting ETL/ELT and data integration pipelines.
• Experience with Google Cloud Platform data services, particularly BigQuery.
• Experience optimizing data pipelines for performance, scalability, reliability, and cost.
• Experience supporting production data systems, including monitoring, logging, incident troubleshooting, and operational support.
• Working knowledge of Git, CI/CD, automated testing, and Agile engineering practices.
• Ability to independently own data engineering work from design through production deployment and ongoing support.
• Strong communication and collaboration skills with the ability to work effectively across technical and business teams.

Preferred Qualifications

• Experience with additional GCP services such as Dataproc, Dataflow, Pub/Sub, or Cloud Storage.
• Experience migrating Hadoop workloads or enterprise data platforms to GCP or another public cloud environment.
• Experience with Spark Streaming, Flink, or similar stream-processing frameworks.
• Experience with data governance, lineage, security, privacy, and data quality frameworks.
• Experience with orchestration tools such as Airflow, Cloud Composer, Oozie, or similar technologies.
• Experience supporting machine learning data pipelines or GCP Vertex AI/MLOps workflows.
• Experience working with high-volume enterprise data in fraud, risk, security, retail, or e-commerce environments.

Benefits

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: https://www.dahlconsulting.com/benefits-w2fta/.

Equal Opportunity Statement

As an equal opportunity employer, Dahl Consulting welcomes candidates of all backgrounds and experiences to apply.