Senior Data Engineer

• This is us
  
  At Qinshift and Avenga we are merging together to start a new era of technology that matter. Leveraging the power of innovations, we are on a journey to shape the future of work, and we are inviting you to co-create it with us.
  
  
  This is you

• Results-driven Data Engineer with expertise in Machine Learning (ML) and Generative AI model training, testing, and implementation within the Snowflake ecosystem. Specialized in building scalable ETL/ELT pipelines, data warehousing, and real-time analytics for financial institutions. Proficient in cloud-based data engineering, orchestration, and optimization of credit risk assessment, fraud detection, and predictive analytics solutions.
• Data modeling and semantic layer design for Power BI & Snowflake
  Cloud-native data architectures (Snowflake, OCI, GCP, AWS, Azure - One of them is enough with focus on OCI)
• Real-time & batch data processing (ClickHouse, PostgreSQL, MySQL, MSSQL)
  - Machine Learning & Advanced Analytics ML model deployment & MLOps (Snowflake ML, Python, Scikit-learn, TensorFlow)
• Credit risk scoring & anomaly detection (Power BI, SQL, Python)
• Predictive analytics & real-time fraud detection
• Feature engineering & model optimization
  - Financial Data & Compliance
• Regulatory compliance (GDPR, FCRA, Basel, KSA regulations)
• Credit risk modeling & real-time risk assessment
• Banking data preparation & fraud detection pipelines
  
• - Development & DevOpsGit, Bitbucket, CI/CD for data workflows
  API integrations & streaming architectures (Kafka, Snowpipe)
• Cost optimization strategies for Snowflake compute usage

This is your role

• ETL/ELT pipeline development using dbt, Airflow, and Python
• Data modeling and semantic layer design for Power BI & Snowflake
• Cloud-native data architectures (Snowflake, OCI, GCP, AWS, Azure - One of them is enough with focus on OCI)Real-time & batch data processing (ClickHouse, PostgreSQL, MySQL, MSSQL)- Machine Learning & Advanced Analytics
• ML model deployment & MLOps (Snowflake ML, Python, Scikit-learn, TensorFlow)Predictive analytics & real-time fraud detectionFeature engineering & model optimization
• Financial Data & ComplianceCredit risk modeling & real-time risk assessmentBanking data preparation & fraud detection pipelines
• Development & DevOps Git, Bitbucket, CI/CD for data workflows
• API integrations & streaming architectures (Kafka, Snowpipe)
• Cost optimization strategies for Snowflake compute usage

 

We take pride in the diverse skills and character of our teams, welcoming everyone to apply and contribute to our collective strength.