Data
Resilient Data Infrastructure
I build the "plumbing" for your data, ensuring it flows correctly from sources to your reporting tools with high integrity and minimal downtime.
Service inclusions
- ETL pipeline automation
- Database optimization (SQL/NoSQL)
- BI tool integration
- Data warehouse mapping
- Engagement
- 3â8 weeks per engagement
- Investment
- Project-based, typically $2kâ$12k
Process
How the engagement runs
A short, written loop with a clear end-of-engagement artefact at every step.
- 01
Data audit
We inventory your current sources, schemas, and reporting tools. I produce a one-page map that names every moving piece and where it tends to break.
- 02
Pipeline design
A written design for the new flows, including source-of-truth decisions, retry/back-off strategy, and the BI tool that will own reporting.
- 03
Build & backfill
I build the pipelines in your own repo and run a one-time backfill so your dashboards have historical context on day one.
- 04
Hand-off & monitoring
Runbooks, monitoring hooks, and a recorded walkthrough for the team that will own the pipelines after handoff.
Deliverables
What you walk away with
Concrete artefacts in your own repository, not slides that age out of relevance.
- ETL pipeline code in your repository
- One-time historical backfill completed
- Runbook covering common failure modes
- Monitoring or alerting hooks into your existing stack
- Recorded walkthrough for the operating team
Fit check
Is this the right engagement for you?
Good fit
- You have a reporting stack that is held together by manual exports
- Your team spends more time fixing data issues than analysing it
- You are moving from a monolith database to a warehouse-style layout
- You want to retire an ETL tool that is no longer maintainable
Probably not a fit
- You need a real-time streaming architecture (Kafka, Flink, etc.)
- You want a fully managed SaaS data platform rolled out across the company
- You are still deciding on your BI tool and want a vendor selection sprint
FAQ
Common questions
Which warehouses and BI tools do you work with?
On the warehouse side it is mostly PostgreSQL, BigQuery, and Snowflake. For BI: Metabase, Apache Superset, Tableau, Power BI, and IBM Cognos for older enterprise setups.
Do you write pipelines in Python or in a managed tool?
Both. I default to Python with SQL where you need full control, and to dbt or Airflow where the team already has the operational muscle. The choice is driven by your team, not mine.
Can you take over an existing broken pipeline?
Yes, that is a common starting point. The first week is usually a triage where I document what is actually broken and propose a phased recovery plan.
How do you handle sensitive data?
I work in your environment, not mine. Secrets stay in your secret manager, sample data is used where possible, and I can sign an NDA or work under your existing DPA.
Ready when you are
Let's map your engagement
Share a short brief by email or WhatsApp and I will reply within one business day with a proposed scope and a time to talk.