100% Remote
Contract Duration: 06 Months contract
- Position is 100% remote - Work US core business hours – 6 Months
Key Skills Required
- Programming: Python, SQL, Spark
- Cloud Platforms: Azure, Snowflake
- Data Tools: DBT, Erwin Data Modeler, Apache Airflow , API Integrations, ADF
- Governance: Data masking, metadata management, SOX compliance
- Soft Skills: Communication, problem-solving, stakeholder engagement
Senior Data Engineer - IRIS
- As IRIS Data Engineer, you will work with Data Scientists and Data Architects to translate prototypes into scalable solutions.
Key Skills Required
- Programming: Python, SQL, Spark
- Cloud Platforms: Azure, Snowflake
- Data Tools: DBT, Erwin Data Modeler, Apache Airflow , API Integrations, ADF
- Governance: Data masking, metadata management, SOX compliance
- Soft Skills: Communication, problem-solving, stakeholder engagement
Technical Skills:
- 7+ years of data engineering or design experience, designing, developing, and deploying scalable enterprise data analytics solutions from source system through ingestion and reporting.
- 5+ years of experience in ML Lifecycle using Azure Kubernetes service, Azure Container Instance service, Azure Data Factory, Azure Monitor, Azure DataBricks building datasets, ML pipelines, experiments, logging, and monitoring. (Including Drifting, Model Adaptation and Data Collection).
- 5+ years of experience in data engineering using Snowflake.
- Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance.
- Experience in building and managing streaming and batch inferencing.
- Proficiency in SQL and any one other programming language (e.g., R, Python, C++, Minitab, SAS, Matlab, VBA – knowledge of optimization engines such as CPLEX or Gurobi is a plus).
- Strong experience with cloud platforms (AWS, Azure, etc.) and containerization technologies (Docker, Kubernetes).
- Experience with CI/CD tools such as GitHub Actions, GitLab, Jenkins, or similar tools.
- Familiarity with security best practices in DevOps and ML Ops.
- Experience in developing and maintaining APIs (e.g.: REST)
- Agile/Scrum operating experience using Azure DevOps.
- Experience with MS Cloud - ML Azure Databricks, Data Factory, Synapse, among others.
