About the Role
Our client, a Fortune 50 retailer and leader in omnichannel commerce, is seeking a Development Engineer to support and modernize demand forecasting capabilities within its digital fulfillment organization. This team develops forecasting solutions that help optimize order volume planning, fulfillment capacity, and workforce utilization across multiple customer fulfillment channels.
In this role, you will collaborate closely with data scientists, machine learning engineers, and platform teams to bridge the gap between research and production. You will help scale forecasting platforms, build robust machine learning pipelines, and improve the reliability, performance, and efficiency of enterprise-scale forecasting systems that directly impact operational planning and customer experience. This position is primarily remote, with a preference for candidates located near the client's hub locations. Limited onsite attendance may be required for local candidates.
Job Description
As a Development Engineer, you will play a key role in designing, implementing, and supporting machine learning and data engineering solutions that power large-scale forecasting platforms.
Responsibilities
- Build, deploy, and maintain machine learning solutions in production environments.
- Develop and optimize time-series forecasting models and supporting data pipelines.
- Convert and scale data-processing workloads from pandas-based frameworks to PySpark and other distributed processing technologies.
- Design and implement ML pipelines using orchestration tools such as Kubeflow Pipelines, Vertex AI, or Airflow.
- Integrate data validation, model training, testing, and deployment processes into automated workflows.
- Optimize data pipelines and storage solutions for scalability, performance, and cost efficiency.
- Collaborate with data science teams to productionize research and analytics solutions.
- Maintain and enhance existing Python applications and codebases following software engineering best practices.
- Build and support CI/CD processes for machine learning workflows and platform deployments.
- Implement cloud-native solutions leveraging Google Cloud Platform (GCP) services and containerized environments.
- Monitor system performance, troubleshoot production issues, and improve operational reliability.
- Contribute to architecture decisions that support enterprise-scale forecasting and fulfillment systems.
Qualifications: Required Qualifications
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field; or equivalent practical experience.
- Experience building and deploying machine learning models in production environments.
- Hands-on experience with time-series forecasting methodologies such as Prophet, ARIMA, or similar frameworks.
- Strong understanding of model validation, experiment tracking, and hyperparameter tuning.
- Experience with feature engineering and feature store concepts.
- Proficiency in Python and software development best practices.
- Experience working with distributed data processing technologies such as Spark, Dask, Ray, or similar platforms.
- Demonstrated ability to transform and scale large datasets using PySpark.
- Experience designing and optimizing data pipelines for performance, reliability, and cost efficiency.
- Working knowledge of analytical data platforms such as BigQuery, including dataset design, partitioning, clustering, and data validation.
- Experience building and maintaining machine learning pipelines using Kubeflow Pipelines (KFP), Vertex AI, Airflow, or similar orchestration platforms.
- Understanding of pipeline architecture, workflow orchestration, caching strategies, and configuration management.
- Experience using Git-based version control and structured change management processes.
- Familiarity with testing frameworks, dependency management tools, and code quality practices.
- Experience working with cloud platforms such as Google Cloud Platform (GCP), AWS, or Azure.
- Knowledge of containerization and orchestration technologies including Docker and Kubernetes.
- Experience implementing CI/CD pipelines and deployment automation.
- Understanding of secrets management and secure environment configuration.
Preferred Qualifications
- Experience with Ray for distributed machine learning training and inference.
- Exposure to Hadoop ecosystem technologies, including Hive, HDFS, or Spark on YARN.
- Knowledge of machine learning model monitoring, observability, and drift detection.
- Experience with infrastructure-as-code tools such as Terraform or Cloud Deployment Manager.
- Familiarity with retail, merchandising, supply chain, fulfillment, or demand forecasting environments.
- Experience partnering with data science teams to productionize research and analytical models.
- Background scaling machine learning applications from prototype environments to enterprise-grade production platforms.
- Experience supporting globally distributed teams across multiple time zones.
- Knowledge of operational readiness practices, including automated alerting, runbooks, and support playbooks.
- Advanced experience tuning application performance and designing highly scalable systems.
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.
Equal Opportunity Statement
As an equal opportunity employer, Dahl Consulting welcomes candidates of all backgrounds and experiences to apply. If this position sounds like the right opportunity for you, we encourage you to take the next step and connect with us. We look forward to meeting you!