Senior DataOps Engineer

Voyagertechnologiesinc; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Voyager Technologies is seeking a DataOps Engineer to design and operate data infrastructure powering analytics and BI. You’ll own end-to-end data pipelines, cloud infrastructure, and data quality in a regulated environment.

Fluency in Python or PowerShell and IaC tools is required, with experience in multi-cloud migrations and GovCloud. The role is remote within the US, with eligibility to obtain US Government Clearance.

Qualifications

• Experience designing and operating data infrastructure for analytics and BI.
• Hands-on coding in Python or PowerShell; confident with IaC tools.
• Experience migrating data workloads across multi-cloud environments.

Responsibilities

• Design, build, and operate end-to-end data pipelines and ETL/ELT workflows.
• Migrate data workloads across AWS GovCloud and Azure Gov/Unified environments.
• Manage cloud resources with Terraform and CloudFormation; configure with Ansible.

Skills

Python

PowerShell

Terraform

CloudFormation

Ansible

Data pipelines

ETL/ELT

Airflow

DBT

Kafka

Azure Event Hubs

Fabric Eventstream

Git

GitHub

Azure DevOps

GitLab

Tools

GitHub

Azure DevOps

GitLab

Job description

Voyager is an innovative space, defense, and national security technology company committed to advancing and delivering transformative, mission-critical solutions. We tackle the most complex challenges to unlock new frontiers for human progress, fortify national security, and protect critical assets to lead in the race for technological and operational superiority from ground to space.

Forge the Future: Join Voyager Technologies

The future belongs to those who build it. At Voyager Technologies, we’re building technologies that protect lives, expand frontiers and prepare us for what’s next. And we’re doing that with people who are wired to solve, build, adapt and lead. These roles are not for the faint of heart.

You’ll help lay the foundation for humanity’s future. Join a culture where innovation thrives, curiosity is rewarded, and impact is real. We’re a company of doers, thinkers and builders, united by purpose and grounded in reality.

If you want to put your skills to work where the stakes are real and the mission is bigger than any one person, forge the future with Voyager.

Job Summary

The DataOps Engineer is responsible for designing, building, and operationalizing data infrastructure that powers the organization’s analytics and business intelligence capabilities. This role sits at the intersection of data engineering, cloud architecture, and DevOps - owning end-to-end data pipelines, platform reliability, data quality, and cloud infrastructure in a regulated environment. Responsibilities span data pipeline development, cloud migrations, platform operations, and infrastructure management across multi-cloud environments.

The ideal candidate is fluent in hands on coding of Python or PowerShell, experienced with infrastructure-as-code tools (Terraform, CloudFormation, Ansible), and comfortable owning both data engineering and cloud infrastructure responsibilities in a cleared, compliance-driven environment. This is a remote position based in the US requiring the ability to obtain a US Government Clearance.

Key Responsibilities

Cloud Migration & Infrastructure

• Lead the end-to-end migration of data workloads from AWS Commercial to AWS GovCloud, including S3, Glue, Redshift, IAM, VPC, Lambda, and CloudWatch.
• Identify and resolve service parity gaps between commercial and GovCloud environments; validate data integrity, encryption posture, and access controls post-migration.
• Set up and administer enterprise Git repositories (GitHub, Azure DevOps, or GitLab) for the data and analytics team, including branching strategy, access controls, and code review workflows.
• Migrate existing scripts, pipelines, and artifacts into version-controlled repositories and enforce DataOps source control best practices across the team.
• Ensure all migrated and provisioned resources comply with NIST 800-171, DFARS, CUI, and ITAR/EAR handling requirements.
• Administer and optimize cloud data services across AWS GovCloud (S3, Glue, Redshift, Lambda) and Azure Government (Synapse, ADF, ADLS Gen2, Databricks).
• Manage infrastructure-as-code for all data platform resources using Terraform and CloudFormation (required); Ansible preferred for configuration management.
• Monitor cloud resource utilization, enforce cost governance, and implement right-sizing recommendations for data workloads.
• Maintain backup, disaster recovery, and business continuity procedures for all data platform services.
• Serve as Tier 3 support for complex cloud infrastructure and data incidents; perform root cause analysis and implement preventive measures.

Data Pipeline Development & Orchestration

• Design, build, and maintain scalable ELT/ETL pipelines using Microsoft Fabric Data Factory, Azure Data Factory, Apache Airflow, and DBT.
• Architect end-to-end data flows from ingestion through transformation to serving layers, ensuring reliability, lineage, and observability.
• Implement real-time and batch data streaming using Apache Kafka, Azure Event Hubs, and Fabric Eventstream.
• Automate pipeline monitoring, alerting, and self-healing mechanisms to minimize manual intervention.

Microsoft Fabric Platform Ownership

• Serve as a platform engineer for Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, and Fabric Notebooks.
• Design and govern OneLake architecture including workspace topology, shortcut strategy, and medallion (Bronze/Silver/Gold) layer design.
• Integrate Fabric with enterprise systems including Microsoft 365 GCC High, Azure Government, Dynamics 365, and third-party data sources.
• Configure Fabric capacity management, workspace permissions, and security in alignment with NIST 800-171 and DFARS requirements.

Data Architecture & Modeling

• Lead the design of enterprise data models including star schema, snowflake schema, and Data Vault 2.0 patterns.
• Architect Lakehouse, data warehouse, and data mesh topologies to serve analytical, operational, and reporting workloads.
• Define and enforce naming conventions, semantic layer s