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Think about a mid-size retail business that pulls its sales numbers from one system, its inventory from another, and its customer data from a third. Every week someone manually exports files from each one, pastes them together in a spreadsheet, and spends an afternoon cleaning up the result before anything useful can be done with it. That process works until it doesn't, and by the time it breaks, the business has been making decisions on data that is days old and held together by a process that lives entirely in one person's head. Data engineering is what replaces that. It is the infrastructure that moves data from where it lives to where it needs to be, automatically, reliably, and in a form that is actually usable.
We build that infrastructure from scratch when necessary and around what already exists when possible. The goal is always the same: data that arrives clean, on time, and without requiring a manual effort every time someone needs to use it.
What This Includes
Designing and building pipelines that extract data from source systems, transform it into a usable structure, and load it where it needs to go, automatically and on a schedule
Automating data collection from external sources including government databases, public records, financial data providers, and web-based sources
Connecting systems that were never designed to talk to each other so that data flows between them without manual intervention
Cleaning and restructuring disorganized or legacy datasets that have accumulated inconsistencies over time
Validating and auditing existing pipelines that have grown unreliable, slow, or difficult to maintain
Building alerting and monitoring so that when something in the pipeline breaks or produces unexpected results, the right person finds out immediately rather than discovering it weeks later in a report
Writing documentation so the system is understandable and maintainable by anyone who works with it in the future
Building pipelines designed for reproducibility, particularly for research environments where the chain from raw data to final output needs to be traceable and verifiable
Recent Work
Pipeline and data infrastructure work here has spanned academic research, small business operations, and large enterprise environments, each with its own distinct set of challenges.
At the academic level, this has included building a large-scale extraction and processing pipeline supporting empirical research on corporate governance, pulling and parsing over 40 years of filings across approximately 3,700 publicly traded companies. The pipeline was designed to run reliably across decades of inconsistently formatted source documents and feed directly into a research workflow where the integrity of every record matters.
At the small business level, the work is often about eliminating a manual process that has become a bottleneck, replacing the weekly spreadsheet export that one person maintains by hand with something that runs on its own and delivers clean results without anyone having to touch it.
At the enterprise level, the challenge is typically integration at scale, connecting systems across large organizations that were built independently over many years and were never designed to share data with each other.
Not sure if this is what you need?
Send us a description of what you're working with and what you're trying to accomplish. We'll give you a straight answer about whether this is the right fit, and what it would actually take to solve it.