DataOps Manager

DataOps & Data Operations Manager

Role Overview:

We are seeking a highly skilled DataOps Leader to spearhead our data operations ecosystem, ensuring end-to-end ownership of the Data Lifecycle.

In this role, you will be responsible for building and managing automated data pipelines (streaming, cleaning, and enrichment) from diverse sources.

You will play a pivotal role in feeding high-quality, accurate information into AI models and mission-critical decision-support systems in a secure, fast-paced environment.

Key Responsibilities

Data Pipeline Management: Design and implement automated ETL/ELT pipelines to orchestrate data flow from various collection sensors to research and operational environments.

Data Labeling & Annotation Operations: * Full ownership of the data labeling "production line" (visual, textual, and signal data).

Defining labeling methodologies, implementing Quality Assurance (QA) protocols, and ensuring high-fidelity "Ground Truth" for model training.

Managing internal labeling teams or coordinating with external/internal annotation platforms.

Data Governance & Standardization: Defining data catalogs, managing metadata, and enforcing Data Quality standards across the entire supply chain.

Security & Classification: Implementing Role-Based Access Control (RBAC), managing permissions, and ensuring all data operations comply with strict defense-sector security protocols and classified environment regulations.

Process Optimization: Monitoring pipeline performance, identifying bottlenecks, and implementing orchestration tools (e.g., Airflow, Prefect) to accelerate Time-to-Insight.

Professional Experience: 3–5 years of experience in Data Management, Data Engineering, or leading complex technological projects.

Technical Proficiency:

Hands-on experience with databases (SQL, NoSQL, Vector DB).

Deep understanding of Cloud or On-premise infrastructures.

Proficiency in Data Versioning (e.g., DVC) and Orchestration tools (e.g., Airflow).

Labeling Management: Proven track record in managing the data labeling lifecycle for Deep Learning / Machine Learning projects.

Programming Skills: Strong proficiency in Python for automation and scripting.

Leadership Skills: Experience leading matrix teams, managing vendors, or supervising specialized task forces.

B.Sc. in Computer Science, Industrial Engineering (Information Systems), or a related technical field.