You will get a self-hosted DAG workflow engine in one Rust binary
Project details
Airflow and Celery fleets are heavy: a Python control plane, a broker, and a cluster to babysit — far more than most pipelines need. I set up dagron, a durable DAG workflow engine that runs from a single static Rust binary with a database as its only state.
You will get
Your workflow defined as plain, version-controlled YAML, validated and cycle-checked before anything runs.
Per-task retries with exponential backoff, timeouts, approval gates, and wait sensors.
Local, Docker, or Kubernetes execution, routed per task by runner class.
A live web UI, GitOps sync, dataset lineage, and (optionally) an MCP server so an AI agent can drive it.
Full handover: deploy docs, a runbook, and a walkthrough call.
Why me: I'm the author of dagron (live at dagron.dev) and a Site Reliability Engineer with 10+ years across AWS, GCP, and Azure. You're hiring the person who built the tool.
You will get
Your workflow defined as plain, version-controlled YAML, validated and cycle-checked before anything runs.
Per-task retries with exponential backoff, timeouts, approval gates, and wait sensors.
Local, Docker, or Kubernetes execution, routed per task by runner class.
A live web UI, GitOps sync, dataset lineage, and (optionally) an MCP server so an AI agent can drive it.
Full handover: deploy docs, a runbook, and a walkthrough call.
Why me: I'm the author of dagron (live at dagron.dev) and a Site Reliability Engineer with 10+ years across AWS, GCP, and Azure. You're hiring the person who built the tool.
Machine Learning Tools
Apache Spark, Azure Machine Learning, Databricks Platform, MLflow, Python, Vertex AIWhat's included
| Service Tiers |
Starter
$350
|
Standard
$950
|
Advanced
$2,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
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GU
Guiscardo U.
Jun 24, 2020
Certification Training Task - Data Analysis and Pipeline Configuration Development
Thanks for the great work and the quick turnaround! Looking forward to more projects together! Best,
AF
Alexander F.
May 28, 2019
Data Engineer to share AWS ETL/ELT best-practices
It was great working with Nicholas, he is an expert when it comes to ETL on AWS and shared some very valuable best-practices. Would be happy to work with him again in the future for more advice or projects.
About Nicholas
Dev/MLOps/SRE engineer
Kuala Lumpur, Malaysia - 4:11 am local time
My core stack is Hadoop, Spark, Hive, and data warehousing, paired with deep AWS expertise and four AWS certifications: Solutions Architect (Associate and Professional), Developer (Associate), and Big Data (Specialty).
How I help clients:
• Design and automate scheduled batch and streaming pipelines end to end, with Python for tooling and glue.
• Lead cloud migrations and integrations — moving existing systems into AWS, or wiring them to it cleanly.
• Ship containerized and streaming workloads on Docker and Kubernetes.
Platform engineering is my specialty. I build internal developer platforms — a clean, self-service overlay on top of cloud and Kubernetes APIs — so your engineers ship faster without fighting the infrastructure underneath.
Tell me what you're building, and I'll help you get it running reliably in the cloud.
Steps for completing your project
After purchasing the project, send requirements so Nicholas can start the project.
Delivery time starts when Nicholas receives requirements from you.
Nicholas works on your project following the steps below.
Revisions may occur after the delivery date.
requirements
requirements
deploy the engine
deploy the engine