ETL and data pipelines that make your data boringly reliable
If you own growth, operations, or product, you already have more data than time. What you usually do not have is a clean, automated way to get that data from dozens of tools into one place you can actually use.
BYBOWU's ETL & Data Pipelines service is a focused part of our Data Engineering & BI offering. From our base in Phoenix, AZ, we work with teams across the US and worldwide to design and implement pipelines that quietly run in the background, keep data in sync, and unlock reliable reporting and analytics.
Common data problems we fix
Most clients come to us with one or more of these headaches:
- Manual CSV exports from tools like Shopify, Stripe, HubSpot, or internal systems just to build basic reports.
- Numbers in dashboards do not match what finance, marketing, or product teams see in their own tools.
- Key metrics such as CAC, LTV, churn, or cohort retention require copy-paste work each week.
- Ad-hoc scripts, cron jobs, and one-off integrations that break silently and no one wants to touch.
- Multiple sources with different schemas and time zones, making any serious analysis or AI work painful.
Our job is to remove the chaos and put in place a clear path from raw data to trusted tables and events that your BI, analytics, and product teams can rely on.
How we design and build your ETL pipelines
We approach pipelines like product work: start from the decisions you want to make, then design the data flows backwards from there.
- Discovery and data mapping. We start with a short but focused inventory of your data sources, key metrics, and consumers. We identify what must be automated now and what can wait for phase 2.
- Architecture and tooling choices. Based on your stack and budget, we propose an architecture that can include batch or streaming pipelines, a data warehouse, and integration points with existing tools. When useful, we align this with your broader DevOps & Cloud setup.
- Pipeline implementation. We build robust extract, transform, and load workflows that handle schema changes, deduplication, and basic quality checks. Where appropriate, we integrate with your APIs or existing Integrations & API Development work.
- Data validation and monitoring. We define validation rules with you, test against known edge cases, and put in place alerts for failures or anomalies so issues are caught early instead of weeks later.
- Handover and iteration. After launch, we document the pipelines, walk your team through how to operate and extend them, and, if you want, stay on to support and improve them over time.
What you get as concrete deliverables
Every ETL & Data Pipelines engagement produces a set of artifacts your team can actually use, not just diagrams that look good in slides:
- Documented list of data sources, destinations, and business-critical metrics.
- Production-ready ETL pipelines for your agreed sources and targets, including schedules and dependencies.
- Clean, modeled tables or views in your warehouse or database, ready for BI tools and analytics.
- Basic data quality checks and error handling, so bad data is flagged rather than silently accepted.
- Runbooks and technical documentation that your internal team can understand and own.
- Optional connections into dashboards and reports, working alongside our Dashboards & Reporting service.
What you can order
- Single-source ETL starter — A focused pipeline for one critical source, for example Shopify, Stripe, or your SaaS product database, into a warehouse or reporting database, with basic cleaning and daily refresh.
- Marketing and sales data hub — Consolidate data from ads platforms, CRM, and website analytics into a single schema designed for CAC, funnel, and ROI reporting, ready for downstream BI tools.
- Product and usage analytics pipeline — Event and account-level data pipelines that prepare clean tables for feature usage, retention, and cohort analysis, often combined with our Product Analytics Implementation.
- Ecommerce and subscriptions pipeline — ETL workflows that reconcile orders, subscriptions, refunds, and payouts from your store and payment providers, aligned with finance and growth reporting needs.
- Data warehouse integration layer — A structured set of staging, transformation, and mart layers for your existing or modernized warehouse, working alongside Data Warehouse Modernization.
- Ongoing pipeline operations — Monthly support to monitor, troubleshoot, and extend your ETL jobs as schemas, tools, and business questions evolve.
How engagement works with BYBOWU
We keep the process simple so you can stay focused on your actual job.
- 1. Intro call. A short conversation to understand your current data stack, main bottlenecks, and timelines. If there is a fit, we move into scoped discovery.
- 2. Discovery and proposal. In one to two working sessions, we map sources, metrics, and consumers. You receive a proposal with clear options, phases, and a budget range. We can usually turn this around within one business day after discovery.
- 3. Build in focused iterations. We implement pipelines in small, testable chunks, so you see value quickly and can adjust as you learn.
- 4. Launch, validate, and document. We run parallel checks against your existing reports, fine-tune transformations, and document how everything works.
- 5. Support or handover. You can keep BYBOWU as your data engineering partner, or we train your internal team and stay available on a lighter maintenance basis.
If you prefer in-person sessions and happen to be near Phoenix, we are happy to whiteboard together. If you are elsewhere in the US or abroad, we handle everything over video and async channels without slowing you down.
Why choose BYBOWU for ETL & data pipelines
- Business-first, not tool-first. We start from the decisions you need to make and the KPIs you track, then design pipelines that support those, rather than pushing a specific vendor or trend.
- Engineering and product thinking together. Our team has built real products, marketplaces, and platforms, not just reports. We understand how data is generated in your apps and where it tends to break.
- Clear communication, minimal drama. You work with senior people who can discuss trade-offs in plain English, keep you informed, and avoid surprises.
- Foundations for future AI and automation. Clean, well-modeled data makes it far easier to explore later work like AI & Automation Solutions without redoing everything from scratch.
- Long-term reliability. We care about monitoring, alerting, and maintenance, and we can pair your pipelines with our Support & Maintenance services if you want a stable long-term setup.
Proof it works in the real world
Marketplace order and inventory flows
For an online marketplace for modern clothing, we helped structure data flows between storefront, catalog, and order management so the team could monitor performance and stock in one place. See project details.
Tactical ecommerce reporting
Working with a tactical apparel marketplace, we organized product and transaction data so sales, marketing, and operations could trust the same set of numbers and plan campaigns with confidence. See project details.
Real-estate platform metrics
On a roommate-finding platform, we aligned user activity and listing data into a structure that made it easier to track activation, usage, and matching performance over time. See project details.
B2B dropshipping operations data
For a wholesaler and dropshipping platform, we standardized product and order data across suppliers and resellers, paving the way for cleaner reporting and operational visibility. See project details.
Questions founders usually ask
What budgets do you usually work with for ETL projects?
Pipeline work ranges a lot. A focused single-source ETL setup is at the lower end, a multi-source marketing or product analytics hub sits in the middle, and a broader warehouse integration layer is higher. Share your constraints and we will suggest a sensible phase 1. You can also review typical ranges on our Prices page.
How long does it take to get something useful live?
We aim to ship value quickly. A simple pipeline from one system into a reporting database can often go live in 2–3 weeks. Multi-source setups that feed curated tables for BI typically take 4–8 weeks depending on complexity and data quality. We confirm milestones and launch windows before we start.
Can you work with our existing warehouse and BI tools?
In most cases, yes. We are comfortable working with existing databases, warehouses, and reporting stacks. Our focus is on defining clean staging and transformation steps so your current BI tools receive trustworthy data without forcing a full rebuild.
What happens if a data source changes its schema or API?
We build pipelines with basic resilience in mind, including checks for missing fields and logging for failures. When a source changes, the pipeline will alert instead of silently corrupting data. If you keep us on for ongoing support, we handle these adjustments for you as part of normal operations.
Will our data be secure and compliant?
We follow sensible security practices, such as limiting access, avoiding unnecessary storage of sensitive fields, and aligning with your existing security policies. If you have specific compliance requirements, we coordinate with your internal team or existing partners to respect those boundaries.
Can you help us use this data for AI or advanced analytics later?
Yes. Clean, well-modeled data is the foundation for any serious AI work. Once your pipelines are in place, our AI Solutions & Custom AI Development team can help explore predictive models, personalization, or other use cases, without redoing your data stack.
Talk through your ETL and data pipeline needs
If you already know which systems need to talk to each other, we can usually outline an initial architecture, rough budget, and timeline within one business day.
If you are still untangling spreadsheets and ad-hoc scripts, we are happy to review your current setup and suggest a pragmatic first phase that gives you reliable numbers quickly.
Contact us for a 24-hour estimate or request a Phoenix data and web audit.
