Senior Data Engineer
Tasks
- Administer Databricks platform and governance policies
- Architect and implement high volume data pipelines
- Build and publish container images for deployments
- Build data quality tests and validation frameworks
- Build machine learning pipelines and MLOps workflows
- Build predictive models using machine learning
- Centralize logging and monitoring with Splunk
- Conduct SAST vulnerability scanning and threat modeling
- Configure IAM roles for secure S3 access
- Deploy applications on OpenShift with TLS and high availability
- Design deep learning architectures and time series forecasting
- Develop NER and enterprise text analysis systems
- Implement CI CD pipelines for reliable code delivery
- Implement vector based search and retrieval systems
- Manage application credentials and secrets
- Monitor post-production model performance
- Orchestrate pipeline scheduling and dependency management
- Perform security compliance assessments across the technology stack
- Remediate vulnerabilities and support security audits
- Track experiments and manage model registry with MLflow
Perks/Benefits
Skills/Tech-stack
Amazon S3 | Apache Airflow | Buildah | CI/CD | CIS Controls | DBT | Databricks | Docker | Entity recognition | FastText | Gensim | Git | IAM | Impact Assessment | Keras | Kubernetes | MLOps | MLflow | Machine Learning | NIST | Named Entity Recognition | OpenShift | OpenShift Secrets | PIA | Pip audit | Privacy Impact Assessment | PySpark | Python | Qualys | Red Hat | SAST | SQL | STRIDE | Scikit-learn | Series forecasting | Skopeo | Snowflake | SonarQube | Splunk | TLS | Tekton | TextBlob | Threat modeling | Time Series | Time Series Forecasting | Transfer Learning | Transformer | Vector Search | XGBoost
Education
Roles
Regions
Countries
States
Cities
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