Your data lives in three systems that don't agree, an analyst spends half the week firefighting pipelines, and the AI initiative is stuck waiting on a foundation that isn't there. That is the problem we solve. Pipelines, data quality, and governance, built fixed-fee and handed back working.
DSE is a senior-only data engineering consultancy serving metro Atlanta: fixed-fee, fixed-scope sprints for mid-market and regulated companies that need a data foundation production AI can actually stand on — not a re-platform for its own sake.
From fintech in Midtown to logistics and manufacturing out toward Gwinnett and Forsyth, metro Atlanta's mid-market is drowning in data it can't rely on. A warehouse here, a SaaS export there, a spreadsheet someone emails on Fridays — and a leadership team that wants AI on top of all of it. The bottleneck is almost never the model. It's a foundation nobody trusts: pipelines that break silently, numbers that don't reconcile, and no governance an auditor would recognize.
The local market for data engineering help is mostly staffing shops that rent you a contractor by the hour and directories full of generalists. A mid-market or regulated Atlanta company needs neither. You need a senior team that scopes the real gap, builds the right pipelines, and leaves you with a foundation your own people can operate — at a fixed fee, with a runbook on exit. That gap is why we built this practice.
We lead with the foundation because the data is where production AI is won or lost. Before we build a single pipeline, we read where your data, governance, and infrastructure actually stand, then sequence the work so the first build is the one most likely to pay off.
Fifty to five hundred employees, with data in systems that don't talk and no standing platform team to make them. You need pipelines that hold, not another hire to firefight them.
A raise or a new line of business has outrun the data stack that got you here. We rebuild the foundation deliberately so the next year of growth doesn't break it again.
Healthcare, finance, and anyone facing SOC 2 or HIPAA. The governance, lineage, and access discipline that satisfies an auditor is the same foundation that lets you scale AI without scaling risk.
The model team is ready and the data isn't. We deliver the AI-ready foundation — quality, governance, and retrieval — so the build that's been waiting can finally ship.
A migration that can't risk a big-bang cutover. We move it in stages with a reversibility plan and a decision log, so production never goes dark and every call is on the record.
You'd rather sit across the table from the engineer doing the work than file a ticket to a national firm. We serve metro Atlanta so that's exactly the relationship you get.
We don't sell a re-platform you don't need. Most Atlanta engagements start with a scoped readiness read, then move into the foundation build or the migration only where the work warrants it — each priced up front, fixed-fee, with a runbook on exit.
A four-to-six-week fixed-scope read on whether your data is ready for AI — a maturity scorecard, a shadow-AI audit, and a prioritized 90-day roadmap. The cheapest way to fund the right build first.
Pipelines, quality contracts, governance, and the warehouse or lakehouse layer — a four-to-twelve-week build scoped to a fixed fee off the readiness read.
Legacy warehouse and pipeline migrations with a reversibility plan and a decision log — moved in stages, handed back for your team to run.
With the foundation sound, route into machine learning or AI engineering — your team's or ours — on data you can finally trust.
Staffing shops give you a body and a rate card. The contractor learns your domain on your budget, makes the load-bearing schema and governance decisions on the fly, and walks out the door with the reasoning when the engagement ends. Senior-only is the opposite. The practitioner who reads your data landscape is the one who designs the pipelines, writes the tests, and documents why every architectural call was made — so the foundation survives the next person who touches it.
Being local in Atlanta matters when the build hits the inevitable wall: an undocumented data source, a compliance constraint that reshapes the model, a stakeholder who owns a system nobody mentioned. You want a partner who can be in the room that afternoon, not a queue three time zones away. We serve metro Atlanta deliberately — Fulton, DeKalb, Gwinnett, Cobb, and Forsyth — because the decisions that make or break a data foundation happen across a table, not over a ticket.
And because the foundation is the whole point, we build it to be governed and observable from day one. Data-quality contracts, lineage an auditor recognizes, and access control your security team signs off on — the same discipline that satisfies a regulator is what lets you put AI on top without inheriting a new class of risk.
Every engagement is fixed-fee and scoped up front. Most start with an AI Readiness Sprint from $12k (four to six weeks), which sizes the foundation work before you commit; the data build that follows is scoped to a fixed fee off that read, typically a four-to-twelve-week sprint. You approve a number — no hourly meter, no open-ended contract.
We serve mid-market and regulated clients across the United States, but we're based in metro Atlanta and built this practice for local buyers who want a senior engineer they can meet in person. Atlanta clients get the closest working relationship; the engineering is the same wherever it's delivered.
Almost never. The fix is rarely more pipelines — it's the right ones, governed and observable, pointed at a use case worth funding. We close the specific gaps the readiness read finds and leave a productive team productive.
AWS-native by default — S3, Glue, Lambda, Step Functions, and the warehouse or lake that fits the workload — with bring-your-cloud on request. Where a team has standardized on the Lakehouse we run on Databricks with Unity Catalog and Delta.
That's the point of the practice. 85% of AI projects fail on data quality and governance, so we fix the foundation first, then route the AI build into our machine learning practice or your own team — on data you can finally trust.
A data engagement that ends in a contractor walking out has left you exactly where you started in twelve months. Ours end in a foundation your team owns: the pipelines and their tests, the data-quality contracts, a governed catalog with access control, lineage an auditor recognizes, and a runbook that documents how every piece works and why it was built that way. Full IP transfer, every time.
That handoff is the whole product. Because the work is senior-only, your engineers see the schema and governance decisions as they're made and inherit a foundation they can extend without re-learning it from scratch. For regulated Atlanta companies, the lineage and access documentation doubles as audit evidence — proof that the data underneath your AI is governed, traceable, and defensible. You are buying a foundation you can operate, not a dependency you have to keep renewing.
Describe the systems that don't agree and the build that's stuck behind them. A principal reads every message, and you'll get a fixed-fee, fixed-scope proposal back within two business days — no junior account manager in between.
We deliver data engineering as fixed-fee, fixed-scope work and hand back full IP with a runbook. Where the engagement touches compliance, "readiness" means a gap assessment mapped to what auditors and regulators ask — never a certification, attestation, or guarantee of an audit outcome.
We are a lean, senior firm. We make a client's data foundation more defensible and observable; we do not run a 24/7 SOC and do not guarantee outcomes we can't control. Where continuous monitoring is needed, it's delivered by a vetted partner the client contracts.