AI Data Engineer
Tasks
- Build data pipelines for AI/ML
- Collaborate with data science and AI platform teams
- Contribute to model validation and improvements
- Design data ingestion and retrieval ready datasets
- Design distributed systems for data consistency and throughput
- Develop ETL and ELT workflows
- Drive delivery of AI ML use cases
- Implement data quality checks
- Maintain Delta Lake and Iceberg tables
- Manage lakehouse data partitioning and file formats
- Monitor production data pipelines
- Participate in governance and risk management
- Process unstructured data for AI consumption
- Support data warehouse and data lake environments
- Troubleshoot and trace pipeline failures
- Tune Apache Spark jobs
Perks/Benefits
- Annual leave
- Continuous learning culture
- Development courses
- Digital wellbeing platform
- Employee assistance programme
- Flexible working
- Life insurance
- Medical insurance
- Parental leave
- Retirement savings
- Sabbatical
- Volunteering leave
Skills/Tech-stack
ADLS | Apache Kafka | Apache Spark | Azure Data | Azure Data Lake | Azure Data Lake Storage | Consistency | Data Lake Storage | Data Modeling | Data Quality | Data integration | Databricks | Delta Lake | Distributed Systems | ELT | ETL | Feature Engineering | Feature Store | Flink | Iceberg | Idempotency | Kinesis | Lakehouse | Latency | Minio | Parquet | Pulsar | PySpark | Python | S3 | SQL | Streaming | System design | Throughput | Unity Catalog | Vector
Education
Roles
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