Manage daily operations of data platforms, including capacity planning, system stability, upgrades, deployments, and disaster recovery to ensure low latency and high uptime.
Design and oversee ingestion from multiple sources such as exchanges and internal or external systems, including protocol decoding and resilient retry logic.
Build rule-based and statistical data validation mechanisms to monitor completeness, uniqueness, time consistency, anomalies, and error handling.
Develop automated processes for data correction, reconciliation, and historical data reprocessing.
Implement monitoring and alerting systems to maintain reliable, production-ready data assets.
Design and sustain end-to-end ETL and ELT pipelines with support for scheduling, caching, data partitioning, modeling, schema evolution, and lineage for both batch and streaming workloads.
Enforce data security through access controls, encryption, audit logging, and data classification to meet regulatory and internal compliance standards, including handling of personally identifiable information.
Utilize Infrastructure-as-Code, data versioning, automated testing, and CI/CD practices to enhance deployment reliability and reduce manual intervention risks.
Support the development of GenAI and large language model-driven data applications for enterprise analytics, data reconciliation, and internal efficiency improvements.
Collaborate with analytics and product teams to integrate and operationalize AI-powered data solutions.

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline.
Minimum of five years of professional experience in data engineering, data platform development, or AI/ML systems architecture.
Extensive experience with cloud-based data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
Proficient in SQL, workflow orchestration tools like Airflow, streaming technologies such as Kafka, and modern data pipeline design principles.
Solid grasp of data warehouse lifecycle management and dimensional modeling techniques.
Proven ability in debugging, performance optimization, and systematic problem resolution.

Practical experience developing foundational data structures for BI and supporting GenAI or large language model systems.
Experience with GitLab and CI/CD pipeline configuration.
Familiarity with data governance, data lineage tracking, privacy controls, and security frameworks.

Competitive salary and benefits package

Virtual/remote position

Part of the data platform and AI engineering team supporting insurance-focused data products

Manage daily operations of data platforms, including capacity planning, system stability, upgrades, deployments, and disaster recovery to ensure low latency and high uptime.
Design and oversee ingestion from multiple sources such as exchanges and internal or external systems, including protocol decoding and resilient retry logic.
Build rule-based and statistical data validation mechanisms to monitor completeness, uniqueness, time consistency, anomalies, and error handling.
Develop automated processes for data correction, reconciliation, and historical data reprocessing.
Implement monitoring and alerting systems to maintain reliable, production-ready data assets.
Design and sustain end-to-end ETL and ELT pipelines with support for scheduling, caching, data partitioning, modeling, schema evolution, and lineage for both batch and streaming workloads.
Enforce data security through access controls, encryption, audit logging, and data classification to meet regulatory and internal compliance standards, including handling of personally identifiable information.
Utilize Infrastructure-as-Code, data versioning, automated testing, and CI/CD practices to enhance deployment reliability and reduce manual intervention risks.
Support the development of GenAI and large language model-driven data applications for enterprise analytics, data reconciliation, and internal efficiency improvements.
Collaborate with analytics and product teams to integrate and operationalize AI-powered data solutions.

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline.
Minimum of five years of professional experience in data engineering, data platform development, or AI/ML systems architecture.
Extensive experience with cloud-based data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
Proficient in SQL, workflow orchestration tools like Airflow, streaming technologies such as Kafka, and modern data pipeline design principles.
Solid grasp of data warehouse lifecycle management and dimensional modeling techniques.
Proven ability in debugging, performance optimization, and systematic problem resolution.

Practical experience developing foundational data structures for BI and supporting GenAI or large language model systems.
Experience with GitLab and CI/CD pipeline configuration.
Familiarity with data governance, data lineage tracking, privacy controls, and security frameworks.

Not specified

AIFT is hiring a Senior Data Engineer, Virtual Insurance