Data Pipeline Engineering
Design, improve, and troubleshoot batch or streaming pipelines with reliability and maintainability in mind.
- Incremental ingestion
- Retry and recovery
- Parameterization
- Backfill strategy
Services
Data Engineering that holds up in production.
Practical engineering support for teams that need reliable pipelines, clearer operations, stronger data quality, or a better cloud data architecture.
Data Platform Architecture
Review data flows, technology choices, environments, scaling paths, security boundaries, and operational trade-offs.
- Azure architecture
- Lakehouse patterns
- Integration design
- Environment strategy
DataOps & Observability
Bring visibility to pipeline health, freshness, SLA performance, failure patterns, and operational bottlenecks.
- Monitoring and alerting
- Run logs and audit trails
- SLA / freshness tracking
- Incident workflows
Data Quality Engineering
Move quality checks closer to the pipeline so downstream teams can trust what they consume.
- Schema validation
- Reconciliation
- Volume / freshness checks
- Anomaly detection
Cloud Data Engineering
Hands-on engineering using modern cloud data technologies, SQL, Python, Spark, and production deployment patterns.
- Azure Data Factory
- Databricks / Spark
- ADLS / Synapse
- SQL and Python
Production Readiness Review
A structured review before launch or after recurring incidents, focused on practical gaps rather than theoretical perfection.
- Failure modes
- Security and secrets
- Performance risks
- Operational runbooks