Hire Data Engineers – Pipelines, Lakehouse & Databricks

Hire Data Engineers Who Have Run a Production Platform

Data engineering is one of the hardest roles to fill locally. The engineers who can model a warehouse, tune a Spark job and keep a few hundred tables landing on time every morning are already employed, and a local search for them stretches into quarters while the reporting backlog grows.

Unosquare staffs nearshore data engineers from delivery centers in Mexico, Colombia, and Bolivia, working US time zones. Time zone overlap matters more for data work than for most disciplines: pipelines fail on your schedule, not on a handover window, and the engineer who owns the DAG needs to be online when an upstream system changes shape.

Every engineer is a full-time Unosquare employee, fluent in English, and backed by our Data & AI Center of Excellence. We hold a 98% client retention rate and a 76 Employee NPS, which means the person who learns your data model is usually still there a year later. See every role we staff on our hire developers hub.

What Our Data Engineers Actually Build

Data engineer is a title that covers a lot of ground. These are the workstreams our engineers own day to day, usually several at once:

• Ingestion: batch loads, incremental extracts and change data capture from operational databases, SaaS APIs, flat files and event streams, using managed connectors such as Fivetran where they fit and Python where the source is awkward.
• ELT and ETL pipelines: landing raw data in the warehouse and reshaping it there, or transforming in flight when volume and latency make that the better trade.
• Warehouse and lakehouse modelling: dimensional models, star schemas, slowly changing dimensions, incremental materializations and the layered raw-to-curated structure that keeps a lakehouse queryable as it grows.
• Streaming: Kafka and Kinesis pipelines for near-real-time data, feeding dashboards, alerting and event-driven services.
• Orchestration: Apache Airflow DAGs with sensible retries, backfills, dependencies and SLAs, plus Azure Data Factory where the stack is Microsoft.
• Transformation with dbt: version-controlled SQL models, tests, documentation and CI, so transformation logic gets reviewed like application code instead of living in one analyst's head.
• Data quality and observability: freshness, volume and schema tests, anomaly alerting, lineage, and pipeline monitoring in tools such as Datadog, CloudWatch and New Relic.
• Governance: access control, PII handling, lineage and audit trails, so an auditor or a regulator can trace a number back to the system that produced it.

This is the same scope our Data & AI Center of Excellence delivers as a practice, staffed onto your team instead of run as a separate project.

Databricks, Snowflake and the Rest of the Stack

Unosquare is a Databricks Partner, and an AWS Consulting Partner. If you are looking to hire Databricks developers or hire Databricks engineers, that means engineers who work in the platform as part of normal delivery: Spark and PySpark workloads, notebook and job cluster workflows, lakehouse table design, and pipelines that land curated data where analysts and models can reach it.

We are deliberately not a single-platform shop. Our data teams build on Snowflake, Databricks, BigQuery and Redshift, streaming and batch, and we pick the platform that fits the use case rather than the other way around. If you already run one, we staff into it. If you are still choosing, our architects will make a recommendation and say plainly what it will cost you to operate.

Relational and non-relational stores turn up in the same brief. Our engineers work across PostgreSQL, MySQL and SQL Server, and NoSQL stores including MongoDB, DynamoDB and Redis, which is usually what teams mean when they ask to hire a NoSQL engineer. The skill worth vetting for is choosing the right store for the access pattern and then modelling for it, not memorising one vendor's syntax.

Data Engineer, Analytics Engineer or Data Scientist?

This is the question that most often sends a hiring brief back for revision. The roles touch the same tables and are not interchangeable, and hiring the wrong one costs a quarter.

Data engineer

Owns the movement and shape of data: ingestion, pipelines, orchestration, warehouse and lakehouse models, and the tests and monitoring that keep them trustworthy. Strongest in SQL, Python, distributed processing and cloud data services. Hire one when data is late, missing, duplicated, expensive to query, or simply unavailable to the people who need it.

Analytics engineer

Sits between engineering and analysis. dbt Labs, who popularised the title, describe the role this way: "Analytics engineers provide clean data sets to end users, modeling data in a way that empowers end users to answer their own questions," and draw the line with analysts directly: "while a data analyst spends their time analyzing data, an analytics engineer spends their time transforming, testing, deploying, and documenting data" (dbt Labs). Hire one when the pipelines run fine but two dashboards disagree about revenue. On our teams this is usually a data engineer with deep dbt and modelling focus rather than a separate headcount.

Data scientist

Answers questions with statistics and models: experimentation, forecasting, segmentation, causal analysis. Consumes what the data engineer produces and is blocked without it. If that is your actual gap, hire data scientists instead.

Machine learning engineer

Puts models into production and keeps them there: training pipelines, serving, monitoring and retraining. If your models work in a notebook but not in production, hire machine learning engineers.

When you need someone else entirely

If the problem is clusters, CI/CD, networking and the environments your data tools run on rather than the data itself, you want platform engineering. If the pipelines are healthy and what is missing is dashboards, definitions and reporting, that is the BI and analytics work covered by our data and analytics services. Telling us which of these you actually need is the fastest route to a useful shortlist.

Seniority and Skills: What to Ask For

Two engineers with the same title can be five years apart in what they can own. We map candidates to the level the work actually needs.

• Mid-level: builds and maintains pipelines against an established pattern. Strong SQL and Python, comfortable in Airflow and dbt, effective inside a model somebody else designed. Right for extending a platform that already has shape.
• Senior: owns a domain end to end. Designs models, makes cost and latency trade-offs, runs migrations and backfills without supervision, and reviews other people's pipelines before they reach production.
• Lead or architect: designs the platform itself. Chooses the warehouse or lakehouse, sets quality and governance standards, plans the way off legacy ETL, and defends the design in your architecture review.

Worth stating explicitly in a brief: your cloud, your warehouse or lakehouse, your orchestrator, whether transformation is dbt or hand-written SQL, streaming or batch, the compliance regime you work under, and whether you want someone to extend a working platform or rebuild one that has stopped working. The more of that you can pin down, the faster the shortlist becomes accurate instead of merely fast.

How We Vet, Match and Onboard

Every Unosquare engineer clears four gates before they meet a client: technical assessment, live coding review, English proficiency and culture fit. For data roles the technical assessment is data work, which means modelling, SQL under realistic constraints and pipeline design questions, not generic algorithm puzzles that tell you nothing about whether someone can run a warehouse.

Matching is done against your stack, not just the title. We look at the warehouse, the orchestrator, the transformation layer, the cloud and the domain, because a Snowflake and dbt engineer moving to a Spark-heavy lakehouse faces a real ramp. We would rather say so up front than place the nearest available body and let you discover it in month two.

Onboarding onto a data platform that already exists

Most of our data engagements join something rather than start one. The first days go into reading the existing DAGs and models, tracing critical tables back to their sources, finding what was never documented, and shadowing an on-call rotation or a month-end close to see where the platform actually hurts. We ask for read access early and a named internal owner for questions. From brief to engineers starting is typically two to four weeks.

Team Shapes and How to Engage

You can hire a single data engineer or stand up an entire data function. Three models, and the right one depends on how much of the thinking you want to keep in-house:

• Capacity: senior engineers added to your existing team. You manage, we strengthen. See staff augmentation services.
• Teams: a self-managed nearshore pod embedded inside your engineering org, with its own lead and quality engineering. See dedicated teams.
• Outcomes: we own a defined deliverable end to end, from discovery through build and release.

Clients typically start with two to five engineers and scale to twenty or more without re-onboarding. For Axos Bank we ran our largest dedicated data engineering team, nine data engineers plus three on QA automation, two business analysts and two scrum masters, delivering master data management, a Customer 360 view and automated ETL on SSIS, Python, Fivetran, dbt, SQL Server and Tableau (case study). For Flatiron Health, a Roche affiliate, one embedded data engineer worked alongside DevOps, software and quality engineers across five or more product teams on Python, SQL and AWS (case study). For Brilliant Earth we built AWS ingestion pipelines, ETL and infrastructure-as-code with monitoring in Datadog, CloudWatch and New Relic, and migrated product data to Shopify with zero downtime and full integrity validation (case study).

Regulated data is routine here: audit-ready pipelines for financial services and HIPAA-aligned flows for healthcare, built to SOC 2 controls.

Frequently Asked Questions

Ready to Build Your Data Team?

Tell us your warehouse, your orchestrator and the problem you are actually trying to fix, and we will come back with engineers who have done that work before and a realistic plan for getting them into your sprints.

Get a Proposal