Reliable Data Pipelines That Work
I design and build dependable data pipelines that turn fragmented, inconsistent, or difficult data sources into structured outputs that can be used reliably in products, operations, analysis, and decision-making.
I work primarily with startups and small companies that need reliable data workflows but do not employ their own dedicated data team.
My services span data extraction, transformation, enrichment, validation, automation, and delivery, with deterministic logic where possible and AI used selectively where it adds value. The goal is to build systems that are transparent, maintainable, and robust enough to handle messy inputs and edge cases and produce output you can trust.
What are data pipelines?
A data pipeline is a system that moves data from where it exists to where it is needed, applying the logic required to make it useful along the way. The easiest way to understand one is to follow the data.
Imagine a platform that helps people discover local classes, events, and activities. The information is out there, but it is scattered across hundreds of websites. Every provider describes what they offer differently. Dates, prices, locations, availability, and booking details appear in different places and formats, and the information keeps changing.
A data pipeline turns that mess into something the product can actually use. It collects the relevant information and converts it into a consistent structure. It can classify listings, fill in missing context, check results for obvious problems, and keep the underlying data up to date.
Not every step should be solved the same way. Explicit algorithms are better when the rules are clear and consistency matters. AI is useful when the system needs to understand language, interpret context, or work with information that does not follow predictable rules. Much of the engineering lies in deciding which approach belongs where, and making the different parts work together reliably.
Now imagine a company with years of valuable technical discussions buried in internal threads. Someone asks a question that has been answered before, perhaps several times, but nobody remembers where. Searching for the right words does not necessarily find the right discussion, and asking an AI model without giving it the right source material risks producing a convincing answer based on the wrong information.
Here, the pipeline has a different job. It organizes the accumulated material so relevant discussions can be found, works out which sources are most useful for a particular question, and gives the AI the evidence it needs before asking it to formulate an answer. The result is not simply "AI answering questions." It is a controlled path from a question, through the right source material, to an answer that remains tied to the underlying information.
The two systems look completely different. In both cases, however, difficult input passes through a sequence of deliberate processing steps and becomes an output that another product, workflow, or person can actually use.
Who needs data pipelines?
Data pipelines become relevant when important work depends on data moving reliably from one place to another. That can happen in a startup, a small business, or a large organization. The trigger is not company size, but whether important work depends on a recurring flow of data.
The examples above show two different versions of this. In the local discovery platform, the product depends on continuously collecting and updating information from many external sources. If that flow stops, the product quickly becomes incomplete or outdated. Once the data itself is part of what customers rely on, manual collection and checking are no longer realistic. The recurring work needs to become an automated system.
In the internal knowledge example, the information already exists, but it is scattered across years of discussion. The challenge is not collecting new data, but making the right material available at the right time, automatically and consistently. This becomes especially important when AI is involved, because the quality of the generated output depends heavily on whether the system has found the right underlying information first.
The same pattern appears in many other settings. A company may need to combine information from several systems before it can be used. It may receive documents that have to be interpreted and converted into structured records. It may rely on external websites or APIs that change over time. It may have recurring processes that still depend on copy-paste, manual checking, or moving information between tools by hand.
At first, these tasks can often be handled manually or through isolated scripts and automations. The need for a pipeline appears when the process becomes recurring, important, or complex enough that those pieces need to work together as one reliable system. The question is no longer simply whether a task can be automated, but whether the flow of data can be made dependable, inspectable, and maintainable enough to keep running with minimal intervention.
For startups and small companies, this stage often arrives before it makes sense to employ a dedicated data team. The company may already have developers, analysts, or technical founders, but still lack the time or expertise to design the data workflows that connect sources, processing steps, AI components, and downstream systems.
These are the kinds of companies I work with: they need their data pipelines built properly, without turning data engineering into a permanent internal function. Once a pipeline is in place, the recurring work is handled by the system itself, with minimal intervention.
Sounds familiar? Let's talk.
Selected Work
Yayando – family activity discovery platform
Built and maintain data pipelines that collect information from diverse external sources, structure and validate it, and deliver it to the live Yayando platform. The system combines conventional algorithms with selective use of AI, primarily for interpreting free text, integrating information from multiple sources, generating content, and classifying media.
Knowledge retrieval system for insurance agents
As part of a project for an insurance company, built a system that allows agents to ask questions and find relevant information from previous internal discussions. The system retrieves the most relevant source material before using AI to formulate an answer, so users can trace the result back to the underlying discussion.
Frequently Asked Questions
How are data pipelines designed?
If a recurring data process is costing time and money because it is manual, fragmented, unreliable, or difficult to scale, the first step is to understand where that cost comes from and what a better process needs to do. Sometimes a small, well-defined part of the process can be automated quickly and already make a substantial difference.
From there, I work out the viable technical approaches and their tradeoffs in cost, reliability, complexity, and maintenance. We then agree on the project scope before implementation starts.
Why not just let AI handle my data?
Because AI is very good at some parts of a data workflow and a poor substitute for others.
AI is useful when a system needs to interpret language, recognize meaning, classify items, or generate outputs. However, AI is inherently non-deterministic, meaning that the same instructions can produce different results from one run to the next. The backbone of data processes is therefore better handled with conventional algorithms that strictly apply the same rules every time.
Cost matters too. Commercial AI models are usually billed based on usage (tokens), so unnecessary AI calls add up when a workflow runs repeatedly.
The ideal approach is to base data workflows on deterministic algorithms, and integrate AI only where semantic reasoning or generation genuinely adds value.
Why not use n8n, Make, or other no-code automation tools?
Sometimes you should. These tools are useful when the process is relatively simple, the systems involved are well supported, and the workflow can be expressed through a manageable number of clear steps.
They become less suitable when the data is messy, the workflow has many exceptions, or reliability depends on more than connecting one system to another. Complex transformations, validation, custom logic, unusual data sources, and controlled AI integration are often easier to build and maintain in a purpose-built system.
Do I need an AI agent?
Often, no.
Most data pipelines perform recurring work with a clear structure: collect information, transform it, validate it, move it between systems, and produce a defined output. These steps usually benefit from predictable logic and tight control.
AI agents become useful when part of the process is genuinely open-ended: choosing between different actions, deciding what to do based on intermediate results, using different tools, or adapting the next step to new information. Tasks with fixed rules and expected outputs usually do not need that level of autonomy.
The most effective systems often combine both approaches. Stable, repeatable work stays deterministic, while AI agents or other AI components are used only where their flexibility creates real value. This keeps the system more reliable and reduces operating cost.
Where does AI actually add value in a data workflow?
AI is most useful when the task depends on meaning rather than fixed rules. That includes interpreting unstructured text, recognizing that differently worded inputs mean the same thing, classifying ambiguous items, extracting information when formats vary, matching related records, summarizing material, or generating new content and structured outputs. These are the parts of a workflow where conventional algorithms tend to become brittle or require an impractical number of rules. AI can handle the variability more naturally.
The key is to use it selectively. When AI is limited to the parts that genuinely require interpretation or generation, the surrounding pipeline can stay predictable, easier to validate, and cheaper to run.
What are RAG and semantic search, and when are they useful?
Semantic search looks for information based on meaning rather than exact keywords. This is useful when the same idea may be expressed in different ways, or when relevant information is buried inside documents, discussion threads, reports, or other large collections of text.
RAG, or Retrieval-Augmented Generation, adds another step. The system first retrieves the most relevant source material, then gives that material to an AI model to generate an answer. This approach gives the model the context it needs to produce answers that are grounded in your own information and easy to verify.
These approaches are useful for internal knowledge bases, technical documentation, customer support material, research collections, and other situations where the right information already exists but is difficult to find and use consistently.
How are data pipelines priced?
There is a wide range of possible project sizes. A pipeline can automate one narrow recurring task or coordinate a much larger process across several systems. Many projects can also be built in stages, with each step delivering useful functionality before the next one is considered. For any project, we agree on the scope and price in advance, so you know what is being built and what it will cost before the work starts.
The cost depends on several factors: the number and variability of data sources, the complexity of the transformations and business logic, the systems that need to be integrated, the volume and frequency of processing, and how the pipeline is deployed and maintained.
Accuracy requirements also matter, and these depend heavily on the domain. A restaurant-finding app may perform perfectly well if a small percentage of records require correction. In a legal, medical, or other high-stakes application, even very high accuracy may be insufficient unless errors can be detected, reviewed, or prevented. Designing for that level of reliability requires additional validation and safeguards.
Running costs are often lower than people expect. Even workflows that use AI can remain inexpensive when the process is designed efficiently and only a limited amount of data needs to be processed by paid models.
What happens after delivery, and what support is available?
After launch, I stay available to fix issues that emerge, answer questions, and make sure the system works properly in real use. This basic post-launch support is typically included in the project price and covers issues with the delivered system and its intended functionality.
Further support remains available as needed and is agreed upon separately. This can include adapting the pipeline when third-party services or requirements change, and adding functionality over time.