Data Scientist / Data Engineer (Full Stack) Location: 4 days on-site, 1 day remote preferred in Greenwood Village, CO Type: Contract with potential for full-time conversion Compensation: $70-$80/hour DOE About the Role Our client is seeking two versatile, full-stack Data Scientists / Data Engineers to join a high-impact team focused on improving enterprise network reliability and user experience. This role is part of a new initiative in collaboration with the Enterprise Network team, analyzing system logs from network devices (e.g., routers, firewalls) to detect anomalies and preemptively identify potential failures. You’ll be working in a fast-paced, data-rich environment, helping to build and scale a pipeline that ingests up to 50TB of raw syslog data per day. The project is currently in its proof-of-concept (POC) phase, with plans to operationalize by mid-to-late summer. Key Responsibilities • Design and implement ETL pipelines using Spark (on AWS EMR) to process raw JSON syslog data from S3 • Parse and transform unstructured logs into structured, meaningful features for modeling • Collaborate with data scientists and network engineers to develop anomaly detection models • Contribute to both data engineering and modeling efforts (initially ~70% ETL / 30% modeling, with flexibility) • Participate in debugging, optimization, and potential future real-time/streaming implementations • Work closely with a cross-functional team including senior data scientists, network engineers, and project leads Ideal Candidate Profile • 3–6+ years of experience in data science, data engineering, or full-stack analytics roles • Strong programming skills in Python or Scala • Hands-on experience with Apache Spark, especially in distributed environments (EMR preferred) • Proficient with AWS services: S3, Glue, EMR, and general cloud-based data workflows • Comfortable working with raw, unstructured data and building pipelines from scratch • Familiarity with basic networking concepts (e.g., routers, firewalls, packets) at the application level • Experience with modeling techniques such as logistic regression, XGBoost, or similar • Ability to work independently and collaboratively in a dynamic, evolving project environment Nice to Have • Prior experience with streaming data or real-time analytics • Exposure to cost optimization in cloud environments • Familiarity with Cribl or similar log ingestion tools • Experience working in POC-to-production transitions