Data Emgineer with DevOps Skill

Role: DataOps Engineer

Location; Hybrid work Dearborn, MI (starting September 1st, will be moving to 4 days a week onsite).

Duration: 12 month contract.

Additional Information:

Hybrid Position Currently 2-3 days a week, but come September 1 st resources will be in office 4 days a week.

Teams Video interview 1 hour 1 round

Job Description:

We are seeking a highly skilled and experienced Senior DataOps Engineer to join our EPEO DataOps team.

This role will be pivotal in designing, building, and maintaining robust, scalable, and secure telemetry data pipelines on Google Cloud Platform (GCP).

The ideal candidate will have a strong background in DataOps principles, deep expertise in GCP data services, and a solid understanding of IT operations, especially within the security and network domains.

You will enable real-time visibility and actionable insights for our security and network operations centers, contributing directly to our operational excellence and threat detection capabilities.

Skills Required:

Code Assessment

GCP

Data Architecture

Endpoint Security

Google Cloud Platform

Data Governance

Cloud Infrastructure

Extract Transform Load (Etl)

Big Query

Network Security

Python

Skills Preferred:

Problem Solving

Critical Thinking

Communications

Cross-functional

Technologies

Cloud Computing

Experience Required:

Core DataOps & Engineering Skills:

Proven experience as a DataOps Engineer, Data Engineer, or similar role, with a strong focus on operationalizing data pipelines.

Expertise in designing, building, and optimizing large-scale data pipelines for both batch and real-time processing.

Strong understanding of DataOps principles, including CI/CD, automation, data quality, data governance, and monitoring.

Proficiency in programming languages commonly used in data engineering, such as Python.

Experience with Infrastructure as Code (IaC) tools (e.g., Terraform) for managing cloud resources.

Solid understanding of data modeling, schema design, and data warehousing concepts (e.g., star schema).

Experience Preferred:

Key Responsibilities:

Design & Development: Lead the design, development, and implementation of high-performance, fault-tolerant telemetry data pipelines for ingesting, processing, and transforming large volumes of IT operational data (logs, metrics, traces) from diverse sources, with a focus on security and network telemetry.

GCP Ecosystem Management: Architect and manage data solutions using a comprehensive suite of GCP services, ensuring optimal performance, cost-efficiency, and scalability. This includes leveraging services like Cloud Pub/Sub for messaging, Dataflow for real-time and batch processing, BigQuery for analytics, Cloud Logging for log management, and Cloud Monitoring for observability.

DataOps Implementation: Drive the adoption and implementation of DataOps best practices, including automation, CI/CD for data pipelines, version control (e.g., Git), automated testing, data quality checks, and robust monitoring and alerting.

Security & Network Focus: Develop specialized pipelines for critical security and network data sources such as VPC Flow Logs, firewall logs, intrusion detection system (IDS) logs, endpoint detection and response (EDR) data, and Security Information and Event Management (SIEM) data (e.g., Google Security Operations / Chronicle).

Data Governance & Security: Implement and enforce data governance, compliance, and security measures, including data encryption (at rest and in transit), access controls (RBAC), data masking, and audit logging to protect sensitive operational data.

Performance Optimization: Continuously monitor, optimize, and troubleshoot data pipelines for performance, reliability, and cost-effectiveness, identifying and resolving bottlenecks.

Education Required:

Bachelor's Degree

Education Preferred:

Collaboration & Mentorship: Collaborate closely with IT operations, security analysts, network engineers, and other data stakeholders to understand data requirements and deliver solutions that meet business needs. Mentor junior engineers and contribute to the team's technical growth.

Documentation: Create and maintain comprehensive documentation for data pipelines, data models, and operational procedures.

Education & Experience:

Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related quantitative field.

Typically, 8+ years of experience in data engineering, with at least 4 years in a Senior or Lead role focused on DataOps or cloud-native data platforms.