Hire Synthetic Data Engineers
Who Generate Useful Data Without Exposing Sensitive Data

You can usually begin the hiring process immediately and receive a shortlist within 24 hours after we understand your data type, target use case, privacy boundary, source constraints, tooling, timeline, and seniority bar. The shortlist is matched to the actual synthetic data problem, such as privacy-safe QA data, tabular data synthesis, LLM evaluation data, simulation scenes, rare-case generation, or ML training augmentation.

Yes. You interview shortlisted engineers before committing. We recommend testing how they profile source data, define constraints, choose generation methods, validate statistical similarity, evaluate privacy risk, handle rare cases, and connect generated data to QA, training, analytics, simulation, or model-evaluation workflows. For a CTO, the best interview signal is whether the engineer can explain what the synthetic data is safe for, what it is not safe for, and how they would prove that.

The first week should produce evidence, not just a generated file. You should see a source-data profile, dataset purpose, initial synthetic sample or generation plan, schema and constraint notes, validation approach, privacy-risk notes, bias or coverage gaps, and a recommendation on the next step. If the dataset is not ready for the intended use, the engineer should say that clearly and show what must change.

A strong Synthetic Data Engineer should deliver useful, governed synthetic data tied to a specific workflow. That may be privacy-safe development data, QA fixtures, LLM evaluation corpora, rare fraud or claims cases, synthetic documents, model-training augmentation, computer vision scenes, or analytics sandboxes. The outcome should be measurable through schema validity, statistical similarity, distribution and relationship checks, privacy-risk review, downstream model or QA impact, bias indicators, and stakeholder review.

Quality is managed through senior screening, role-specific interview criteria, data review, code review, documented assumptions, validation artifacts, and delivery checkpoints. For synthetic data work, we look for evidence that the engineer can preserve the patterns that matter while avoiding unsafe copying. We also look for clear documentation of source assumptions, privacy tradeoffs, generation parameters, validation metrics, known gaps, and rules for when the dataset can or cannot be used.

Yes. The engineer can work inside your data warehouse, lakehouse, notebooks, repositories, CI systems, QA tooling, model-evaluation stack, privacy review process, and communication channels. Common tool contexts include Python, SQL, dbt, Spark, Snowflake, BigQuery, Databricks, SDV, Gretel, OpenAI eval workflows, custom simulators, Omniverse Replicator-style scene generation, and internal data platforms. We define access boundaries before onboarding.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, dataset review, privacy review, validation review, and handoff. For synthetic data engagements, overlap is especially useful when domain experts, legal, security, product, QA, and ML owners need to inspect the same dataset decision before it is used.

NDA and IP assignment are handled before onboarding. Access is scoped to the minimum required data, metadata, repositories, tooling, and environments. In many engagements, the engineer can start with schemas, redacted samples, profiling outputs, or controlled extracts before touching sensitive source data. Sensitive work follows your security rules, audit expectations, approval process, retention rules, and data-sharing constraints.

Use the risk-free trial to evaluate whether the engineer can reason clearly about source data, privacy risk, validation, downstream use, and technical tradeoffs. If the engineer produces data without explaining utility, privacy, known gaps, or acceptance criteria, that is not a good fit for this role. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one Synthetic Data Engineer for a focused dataset or build a pod when the work spans data engineering, ML engineering, privacy review, QA automation, simulation, or platform integration. For example, a synthetic data specialist might design generation and validation while a data engineer handles pipelines, an ML engineer measures model impact, and a privacy engineer reviews re-identification risk.

Typical options include a Synthetic Pilot at $16,000 fixed scope, a Senior Synthetic Data Engineer from $5,000 per month, or a Synth + ML + Privacy pod from $13,500 per month. We confirm the right model after discovery so you can compare a focused dataset pilot, embedded specialist, or cross-functional pod against the business risk and timeline of your actual synthetic data requirement.

We can support both models. If you already have strong data, ML, QA, or product leadership, the engineer can plug into your process. If you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, and senior technical review around source assumptions, generation milestones, validation results, privacy-risk decisions, and dataset handoff.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist engineering talent. For synthetic data, that matters because weak work can create a false sense of safety: data may look realistic while leaking sensitive patterns, breaking relationships, distorting model behavior, or failing to improve test coverage. You get a shorter path to qualified candidates and a trial structure focused on evidence, not resume volume.

Devlyn is a better fit when the synthetic data work affects customer data, regulated workflows, model releases, QA coverage, vendor sharing, security posture, or long-term data governance. You get vetting, replacement support, delivery governance, IP protection, and continuity around outcomes such as useful synthetic datasets, validation reports, privacy-risk review, generation pipelines, and repeatable dataset operations.

Hire a Synthetic Data Engineer when the core problem is generating realistic, validated, privacy-aware data for a specific use case. A Data Engineer is often the right hire for pipelines, modeling, and warehouse reliability. An ML Engineer is often the right hire for model training, deployment, and evaluation. A Synthetic Data Engineer is the better fit when you need controlled data generation, schema and constraint preservation, privacy-risk reduction, long-tail scenario creation, simulation data, AI eval corpora, or QA datasets that replace unsafe production copies.