Data Engineering & Pipelines on Databricks, dbt & Airflow
Turn Raw Data Into Real Insight
Data engineering transforms raw, scattered data into clean, reliable streams your business can act on. I build and automate pipelines using Airflow, dbt, Databricks, and cloud-native tools so your data arrives on time, in the right shape, every time. The result: less firefighting, faster reporting, and a foundation that scales with you.
Messy data? Incomplete pipelines? I help teams build streamlined, automated data systems that are fast, reliable, and easy to scale. The goal is simple: deliver clean, usable data to the right people at the right time.
What You Get
- Written gap analysis and pipeline architecture diagram
- Airflow DAGs with retry logic, SLA monitoring, and Slack/email alerts
- dbt models with schema tests, documentation, and lineage graph
- Databricks notebooks or Spark jobs for heavy transformation
- Runbook covering operations, reprocessing, and common failure modes
- 30-day post-launch support via Slack
Fixing the Pain Points
Pipelines breaking silently, reports running hours late
Engineers at mid-size companies spend 5–15 hours per week chasing data quality failures. I replace ad-hoc scripts with monitored, retry-safe Airflow DAGs and dbt tests that catch issues at source.
No single source of truth
When five teams query five different models and get five different revenue numbers, decisions stall. I implement a Medallion architecture (Bronze → Silver → Gold) so every consumer reads from the same validated Gold layer.
Can't scale to daily or real-time loads
Batch jobs that take 4 hours at 10 GB will take 40 hours at 100 GB. I re-architect for partitioned, incremental loads on Spark/Databricks so throughput scales linearly, not quadratically.
My Engineering Principles
Automated, tested pipelines
Every DAG ships with dbt schema tests and Great Expectations checks. No deploy goes out without a green test suite.
Observable by design
Airflow alerts, dbt run results, and Databricks job metrics feed into a single Slack/email channel. You know about failures before your users do.
Incremental by default
Full refreshes are expensive and fragile. I model everything as idempotent incremental loads so reprocessing a broken run is a one-command fix.
My Approach
Discovery call + data audit (weeks 1–2)
I map your sources, schemas, and current orchestration. You get a written gap analysis with priority ranking.
Architecture design + tooling sign-off (weeks 2–3)
You review and approve the proposed stack before a line of code is written.
Pipeline build + testing (weeks 3–10)
I build in two-week sprints with regular Slack updates. Each sprint ends with a demo of working pipelines.
Handover + runbook (final 1–2 weeks)
I write the runbook, train your team on the tooling, and stay on Slack for 30 days post-launch.
Glossary
- dbt (data build tool)
- An open-source transformation framework that lets you write data models in SQL and test, document, and version them like software. The de facto standard for the T in ELT.
- Apache Airflow
- An open-source workflow orchestrator that schedules and monitors data pipelines as directed acyclic graphs (DAGs). Used to coordinate jobs across Spark, dbt, APIs, and cloud services.
- Databricks
- A unified analytics platform built on Apache Spark, offering collaborative notebooks, Delta Lake storage, and managed clusters for large-scale data engineering and ML workloads.
- Medallion architecture
- A layered data design pattern (Bronze → Silver → Gold) that progressively cleans and enriches raw data into business-ready tables inside a lakehouse.
- ELT (Extract, Load, Transform)
- A data integration pattern where raw data is loaded into the target platform first and transformed there. ELT is the standard approach in cloud warehouses and lakehouses.
Common Questions
How long does a data engineering engagement typically take?
Most data engineering projects run 6–12 weeks. A pipeline modernisation (replacing ad-hoc scripts with Airflow + dbt) is typically 6–8 weeks. A full lakehouse build on Databricks runs 8–14 weeks. I scope precisely after a one-hour discovery call.
Do you work with our existing Snowflake / BigQuery / Redshift setup?
Yes. I work with whichever cloud warehouse you already use. The orchestration and transformation layer (Airflow, dbt) is platform-agnostic. I recommend a migration only if the current warehouse is a genuine bottleneck, not by default.
What does data engineering cost?
Pricing depends on scope, source system complexity, and whether you need ongoing retainer support. I work on a project or retainer basis and provide a fixed-price estimate after the discovery call.
Ready to Build Better Data Systems?
Let's discuss how I can help you modernize your data infrastructure and unlock the full potential of your data.
Schedule a Free Consultation