Engineering & Data

Data Agent

Autonomous data pipeline construction and analytics

Data Agents build, maintain, and optimise data infrastructure autonomously. They ingest data from any source, transform it through complex ETL pipelines, maintain data quality, generate analytics dashboards, and answer natural language queries against enterprise data warehouses — turning raw data into actionable intelligence without manual engineering.

2,000+

Pipelines Managed

97%

Query Accuracy

99.9%

Pipeline Uptime

-88%

Time to Insight

Core Capabilities

Automated ETL pipeline construction from source discovery through transformation to warehouse loading

Natural language querying — convert business questions into optimised SQL across complex schemas

Data quality monitoring with anomaly detection, freshness tracking, and lineage documentation

Dashboard generation — create interactive visualisations from natural language descriptions

Schema evolution management — handle schema changes across pipelines without breaking downstream consumers

Cost optimisation — analyse query patterns and recommend partitioning, indexing, and materialisation strategies

Use Cases

Business intelligence — answer ad-hoc analytical questions without waiting for analyst availability

Data engineering — build and maintain ETL pipelines from new data sources automatically

Reporting automation — generate recurring reports with commentary and trend analysis

Data migration — move data between systems with automated mapping, validation, and reconciliation

Compliance reporting — generate regulatory reports from raw data with audit trail documentation

Predictive analytics — build and deploy forecasting models from historical data patterns

How It Works

Source Discovery

Agents connect to data sources, profile schemas, sample data, assess quality, and build a comprehensive catalogue of available datasets.

Pipeline Construction

ETL pipelines are generated using best practices — idempotent transformations, schema validation, error handling, and incremental loading strategies.

Query & Analysis

Natural language questions are converted to optimised SQL, executed against the warehouse, and results are formatted with explanatory commentary.

Monitoring & Maintenance

Pipelines are continuously monitored for failures, data quality issues, and performance degradation with automated remediation.

Technology Stack

dbtApache SparkSQL GenerationData ProfilingOrchestration

Integrations

SnowflakeBigQueryDatabricksRedshiftFivetranLooker

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