26-2253: Data Engineer, AI/BI: Hyderabad, India
Tasks
- Architect and manage data lake and data warehouse solutions
- Build and maintain feature stores
- Build and optimize analytical views and materialized models
- Construct and curate training and evaluation datasets
- Design build and maintain ELT ETL pipelines
- Develop semantic layers and BI ready data marts
- Ensure data quality lineage and observability
- Implement RBAC data masking and PII governance
- Implement data versioning and governance for AI compliance
- Implement real time and streaming data pipelines
- Integrate BI platforms with data warehouse sources
- Manage cloud data infrastructure with infrastructure as code
- Manage vector databases and embedding pipelines
- Monitor pipeline health data freshness and cost efficiency
- Support MLOps workflows with experiment tracking and model registries
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Kinesis | Access Control | Airflow | Amazon Redshift | Apache Beam | Apache Flink | Apache Kafka | Apache Spark | Azure Event | Azure Event Hubs | Azure ML | BigQuery | DBT | Dagster | Data Pipelines | Data masking | DataOps | Databricks | Databricks Feature Store | Distributed Processing | Embedding pipelines | Event Hubs | Feast | Feature Engineering | Feature Store | Git | Great Expectations | Infrastructure as Code | Kubeflow | LLM Data Pipelines | MLflow | Monte Carlo | PGVector | Pandas | Pinecone | Prefect | Pulumi | PySpark | Python | RBAC | Role-Based Access | Role-Based Access Control | SQL | SQLAlchemy | SageMaker | Snowflake | Tecton | Terraform | Vector Databases | Vertex AI | Weaviate | “as-code”
Education
N/A
Roles
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