01
Data Pipeline Engineering
Batch and incremental pipelines across APIs, databases, files, SFTP and cloud storage, with reliable retries, logging and operational controls.
Services
Specialist data engineering, where it matters most.
Focused delivery across ingestion, transformation, Azure orchestration, Databricks and production operations.
02
dbt & Analytics Engineering
Project setup, staging/intermediate/mart layers, incremental models, tests, documentation, macros and maintainability reviews.
03
Databricks Engineering
PySpark, Delta Lake, workflows, performance tuning, migration support and production lakehouse engineering.
04
Azure Data Factory
ADF orchestration, reusable patterns, dynamic pipelines, source integration, production troubleshooting and modernization.
05
Data Platform Modernization
Pragmatic modernization of legacy ETL, SQL-heavy workflows and fragmented ingestion processes into maintainable cloud patterns.
06
DataOps & Production Support
Monitoring, incident support, missing-file handling, schema change response, data-quality checks and reliability improvement.
Start small when that makes sense.
Not every engagement needs to begin as a large transformation. A focused review, pipeline fix or well-scoped implementation can be the right first step.
Platform Health Check
Review reliability, architecture, performance, data quality and technical debt, then leave with a prioritized improvement plan.
Pipeline Rescue
Diagnose recurring failures, stale data, duplicate loads or slow runs and stabilize the affected workflow.
Implementation Sprint
Deliver a tightly scoped ingestion, dbt or Databricks workstream with clear acceptance criteria.