Senior Data & Machine Learning Engineer

Sentra is the global leader in cloud-native data security for the AI era. The company’s mission is to empower organizations to confidently scale their data operations across multi-cloud and on-premises environments while leveraging the power of AI without compromising security.

Sentra’s unique approach enables enterprises to autonomously scan their environments without the need for agents, ensuring that data remains securely within their cloud or on-premises infrastructure. This innovative methodology sets us apart in the industry, providing organizations with control and visibility over their sensitive data at all times. Our commitment to excellence in data security posture management and data detection and response makes Sentra a leader in the field.

About the Rol

eWe are looking for a Senior Data & Machine Learning Engineer to operate at the intersection of data platform engineering and machine learning enablement. This role is responsible for building scalable, efficient, and reliable data systems while enabling Data Science and Analytics teams to develop and deploy ML-driven features

.You will take ownership of the data and ML infrastructure layer, ensuring that pipelines, storage models, and compute usage are optimized, while also shaping how data workflows and ML solutions are designed across the organization

.
Key Responsibiliti

esData Platform & Infrastructu

• reDesign, build, and maintain scalable data pipelines and storage systems supporting analytics and ML use cas
• esEnsure compute and cost efficiency across pipelines, storage models, and processing workflo
• wsOwn and improve data orchestration, transformation, and serving layers (e.g., Spark, DBT, streaming/batch system
• s)Build and maintain shared infrastructure components, includin
• g:IO managers and data access abstractio
• nsIntegrations with DBT, Spark, and other data framewor
• ksInternal tooling to improve developer productivity and reliabili

ty
ML Enablement & Collaborat

• ionPartner closely with Data Science to design and productionize ML solutions for new features and research initiati
• vesTranslate experimental models into robust, scalable production syst
• emsSupport feature engineering, training pipelines, and inference workfl
• owsHelp define best practices for ML lifecycle management (training, validation, deployment, monitori

ng)
Data Quality, Governance & Best Pract

• icesEnforce best practices for building and maintaining data processes across Data Analyst and Data Science t
• eamsDefine standards
• for:Data modeling and transformat
• ionsPipeline reliability and observabi
• lityTesting, versioning, and documenta
• tionImprove data quality, consistency, and discoverability across the organiza

tion
Performance & Reliab

• ilityOptimize systems for performance, scalability, and cost effic
• iencyMonitor and troubleshoot data pipelines and ML systems in produ
• ctionImplement observability (logging, metrics, alerting) across data work

flows
Required Qualific

• ationsStrong programming skills in Python (or similar lan
• guage)Proven experience building and maintaining production-grade data pip
• elinesHands-on experience with data processing frameworks (e.g., Spark or si
• milar)Familiarity with DBT or modern data transformation wor
• kflowsExperience working with cloud environments (AWS, GCP, or
• Azure)Solid understanding of data modeling, distributed systems, and ETL/ELT pa

tterns
Preferred Qualifi

• cationsExperience productionizing machine learning models and pi
• pelinesFamiliarity with orchestration tools (e.g., Airflow, Dagster, P
• refect)Experience building internal platforms or developer
• toolingKnowledge of feature stores, model serving, or real-time inference
• systemsExperience optimizing compute costs and performance a
• t scaleBackground working closely with Data Science and Analytic

s teams
What Success Lo

• oks LikeData pipelines are reliable, observable, and cost-e
• fficientData Science teams can move faster from research to pr
• oductionClear and enforced best practices across data w
• orkflowsShared infrastructure reduces duplication and improves developer
• velocityML-powered features are robust, scalable, and maintainable in pr