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Hire data engineers and data scientists who ship data the business can trust.
TekRecruiter is a data engineering and data science recruiting agency focused solely on technology and engineering roles at tech and SaaS companies. We separate the engineering from the science before a search starts, so you hire the person your roadmap needs: the one who builds the pipelines, models the metrics or puts a model into production. Beyond skills and experience, we look for HEARTThe HEART standardHHigh agencyEExecutionAAccountabilityRResourcefulnessTTransparencyWhat we look for, beyond skills →.
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What is a data engineer?
A data engineer builds and runs the pipelines, storage and platforms that move data from source systems into a warehouse or lakehouse, on schedule and correct, so analysts, data scientists and products can rely on it. It is an engineering job, not a science job. A data scientist uses that data to predict and explain; an analytics engineer turns it into the tested tables and metrics the business decides from.
- Data engineer
- Builds ingestion, batch and streaming pipelines and the warehouse or lakehouse underneath, and keeps them reliable and affordable.
- Analytics engineer
- Also called a data analytics engineer. Models raw data into tested, documented tables and metrics, with a data engineer’s skills. Not a BI analyst or reports developer.
- Data scientist
- Applies statistics and machine learning to forecast, predict and explain, then puts the models to work in the product or the business.
What companies hire data engineers to do.
Most data searches come down to one of these six mandates. Naming the mandate tells you whether you need engineering, analytics engineering or science.
01 · Pipelines and ingestion
Data that lands on time, every time.
Reliable ingestion from product databases, SaaS tools and event streams, with pipelines that rerun safely and backfill without duplicates.
- Freshness targets per source, with alerts when a load is late
- Idempotent jobs, so a rerun fixes the problem instead of doubling it
- Airflow
- Dagster
- Fivetran
- Airbyte
- Spark
02 · Warehouse and lakehouse
One platform, not five copies of the truth.
A warehouse or lakehouse sized to the workload, with open table formats where they fit and compute spend that engineering can explain.
- Migrations off legacy warehouses and on-premises Hadoop
- Cost per query and per team tracked, not discovered on the invoice
- Snowflake
- Databricks
- BigQuery
- Redshift
- Apache Iceberg
- Delta Lake
03 · Analytics engineering
Revenue means the same thing in every meeting.
Raw tables modeled into clean, version-controlled, tested models and one set of metric definitions, so finance, product and sales stop arguing about the number.
- Models in code review, with tests and documentation on every change
- A semantic layer that BI tools and AI assistants query the same way
- dbt
- dbt Semantic Layer
- LookML
- Cube
- SQL
04 · Data quality and contracts
Break the pipeline, not the dashboard.
Schema and quality checks at the source, and data contracts with the teams that produce the data, so a renamed column fails a build instead of a board deck.
- Contracts on schema, semantics and freshness, owned by producers
- Lineage that shows which reports a failing table touches
- dbt tests
- Great Expectations
- Soda
- Monte Carlo
- OpenLineage
05 · Streaming and real time
Decisions in seconds, not overnight.
Event streams for fraud checks, pricing, personalization and operational dashboards, where yesterday’s batch is too late.
- Exactly-once or at-least-once semantics chosen on purpose
- Schema evolution handled without breaking consumers
- Kafka
- Confluent
- Flink
- Spark Structured Streaming
- Kinesis
06 · Data science and ML
Models that move a number the business watches.
Forecasting, pricing, risk and churn models that leave the notebook, run in production and get measured against the decision they were built for.
- Experiment design and evaluation before deployment
- Monitoring for drift once the model is live
- Python
- scikit-learn
- XGBoost
- MLflow
- SageMaker
What to know before you hire a data engineer or data scientist.
The questions that decide whether a data search ends in the right hire, whether or not you run it with us.
Data engineer, analytics engineer or data scientist?
The most common mistake we see is assuming every data engineer is a data scientist, and every data scientist is a data engineer. One is engineering, the other is science. Some people have both, most lean one way. If the data isn’t reliable yet, you need engineering. If it’s reliable but nobody agrees on the numbers, you need an analytics engineer. If you need predictions, you need a scientist.
Who should be your first data hire?
Usually a data engineer or an analytics engineer. A data scientist hired before the data is clean spends the first months building pipelines they weren’t hired to build. Get the sources flowing into a warehouse and the core metrics modeled, then hire science against a specific question the business wants answered.
What does a data scientist cost?
In TekRecruiter’s 2026 placements, mid-to-senior data scientists working in Python and machine learning averaged a $155,000 base salary in South Florida, across seven placements. That figure is for data scientists only; we don’t yet publish a separate data engineer figure. Full tables: 2026 Salary & Rate Guide.
How should you interview a data engineer?
Ask them to walk one pipeline end to end: the sources, how often it ran, how they knew the data was right, the day it broke, and how they backfilled. Then ask what it cost to run and what they did about it. Engineers who have owned production data answer with specifics; the rest describe tools.
What does a modern data stack look like in 2026?
Typically ELT into Snowflake, Databricks or BigQuery, transformations in dbt, orchestration in Airflow or Dagster, Kafka for streaming, open table formats such as Apache Iceberg, a semantic layer for metrics, and data contracts with producing teams. Hire for the patterns behind the stack, not the logos, because the logos change.
Is an analytics engineer just a BI analyst who knows SQL?
No. A data analytics engineer turns data into actionable insight with a data engineer’s skill set: version control, testing, modeling and pipeline design. A data analyst answers questions from the data, a BI analyst or reports developer builds dashboards on top of it, and an analytics engineer builds the trusted layer they all depend on.
Why data searches go wrong.
Data titles overlap more than almost any other in engineering. These are the mismatches we see most often.
- 01
Engineering hired, science needed.
Or the reverse. The job post said data, and nobody decided which.
- 02
A scientist with nothing to model.
Months spent cleaning data a data engineer should have owned.
- 03
Dashboards nobody trusts.
Three definitions of revenue, and no analytics engineer to settle them.
- 04
An analyst title on an engineering job.
Strong SQL, but no testing, version control or pipeline design.
- Avg data scientist base, South Florida, 2026
- $155K
- Data leadership placements in 2026
- VP + Director
We follow the data all the way to the decision.
We’re technical people, and we take every candidate through the product their data served: where it came from, how they proved it was right, what it cost to run, and which decision it changed. That tells us quickly whether someone is an engineer, a scientist or both. Every data engineer and scientist we present also meets our HEART standard.
- The product behind the data: who used it, what they decided with it, and what happened when it was late or wrong.
- Source to decision: the full path they built or modeled, and where they sat in it.
- Engineering or science: which one they have done for real, and whether they have both.
- Reliability and cost: freshness, failures, backfills and what they did to the warehouse bill.
| A generalist IT staffing firm | TekRecruiter |
|---|---|
| Treats data engineer and data scientist as one search | Scopes engineering, analytics engineering and science separately |
| Matches on SQL, Python and Spark | Walks a pipeline from source to the decision it fed |
| Sends BI analysts for analytics engineering roles | Screens for modeling, testing and version control |
| Ignores what the platform costs to run | Asks what they did about compute spend and freshness |
| Stops at individual contributors | Also recruits Heads, Directors and VPs of Data |
The HEART standard
What we look for beyond skills and experience, in every candidate we present.
- High agencyPeople who see what needs to be done and act without waiting to be told.
- ExecutionPeople who turn ideas into results.
- AccountabilityPeople who own the outcome, not just their piece of the work.
- ResourcefulnessPeople who figure things out when the answer isn’t obvious.
- TransparencyPeople who communicate clearly, honestly, and early.
What we screen out.
The patterns that separate someone who has owned production data from someone who has queried it.
A tool tour, not a pipeline
Lists Snowflake, dbt and Airflow but can’t walk one pipeline from source to consumer.
Never owned a broken load
No story about a late or wrong dataset, the backfill, or the fix that followed.
No tests on data
Trusts whatever lands, with no schema, freshness or quality checks.
Dashboards as the deliverable
An analyst’s portfolio presented for an engineering or analytics engineering role.
Notebook-only models
Models that were never deployed, monitored or measured against a business result.
Can’t define the metric
Builds the table but can’t say what counts as an active customer, or why.
Senior, staff and principal data engineers.
Level is about how much of the data platform someone owns, not years of SQL.
Senior
Owns pipelines and models end to end, in production.
- Designs and ships pipelines and models without hand-holding
- Sets freshness and quality targets and meets them
- Reviews other engineers’ models and pipelines
Staff
Sets the data architecture across teams.
- Chooses the warehouse, table formats and orchestration standards
- Brings data contracts to the teams that produce data
- Owns platform cost and reliability as a whole
Principal
Shapes how the company builds on data.
- Multi-year calls: lakehouse, real time, data for AI
- Advises leadership on data risk, governance and spend
- Defines what trusted data means across the business
Our 2026 data point is for data scientists: a $155K average base in South Florida. See the 2026 Salary & Rate Guide.
Data engineer vs. analytics engineer vs. data scientist (vs. data analyst).
| Data engineer | Analytics engineer | Data scientist | Data analyst | |
|---|---|---|---|---|
| Core question | Is the data there, on time and correct? | Does every team get the same trusted number? | What will happen, and why? | What happened? |
| Builds | Pipelines, streaming and the warehouse or lakehouse | Tested data models, metrics and the semantic layer | Forecasting, prediction and ML models | Analyses, reports and dashboards |
| Typical stack | Python, SQL, Spark, Kafka, Airflow or Dagster | SQL, dbt, Git, a semantic layer | Python, statistics, scikit-learn, MLflow | SQL, spreadsheets, Looker, Tableau or Power BI |
| Engineering depth | High | High, focused on modeling | Varies; strongest ones deploy their own models | Low to moderate |
| Hire when | Data is missing, late or unreliable | Nobody agrees on the numbers | You need predictions tied to a decision | You need answers from data that’s already trusted |
How to hire data engineers and data scientists with us.
Direct hire
A permanent data engineer, analytics engineer or scientist to own the platform, backed by a 90-day guarantee.
Direct hireStaff augmentation
A contract data engineer for a warehouse migration, a dbt rebuild or a streaming project with a deadline.
Staff augmentationContract-to-hire
Start the platform work now, then convert the engineer who proved it with your data.
Contract-to-hireExecutive search
Heads, Directors and VPs of Data, Analytics and Data Science.
Data leadership
Data engineering and data science hiring questions, answered.
What is the difference between a data engineer, an analytics engineer and a data scientist?
A data engineer builds and runs the pipelines and platforms that move and store data. An analytics engineer, sometimes called a data analytics engineer, models that data into tested tables and shared metrics. A data scientist uses statistics and machine learning to predict and explain. Engineering and science are different skills; some people have both, and a search should say which it needs.
What is an analytics engineer?
An analytics engineer turns raw data into the trusted, documented models and metrics a business decides from, using a data engineer’s tools: SQL, dbt, version control, testing and code review. It is not a data analyst, a BI analyst or a reports developer. Those roles consume the models; the analytics engineer builds them.
What do data scientists earn in 2026?
In TekRecruiter’s 2026 placements, mid-to-senior data scientists working in Python and machine learning averaged a $155,000 base salary in South Florida, across seven placements in Miami, Fort Lauderdale and West Palm Beach. See the 2026 Salary & Rate Guide.
Should our first data hire be a data engineer or a data scientist?
In most companies, a data engineer or analytics engineer comes first. Scientists need reliable, modeled data to do their job, and without it they spend their time building pipelines. Hire a data scientist first only when clean data already exists and there is a specific prediction the business needs.
Do you recruit data engineers and data scientists in South Florida?
Yes. TekRecruiter is headquartered in Miami, and our 2026 data scientist placements were in Miami, Fort Lauderdale and West Palm Beach, averaging a $155,000 base. We recruit data engineers and data scientists for companies in New York and Boston too, on site, hybrid or remote.
Do you recruit data leaders?
Yes. In 2026 we placed a VP of Data & Analytics at a fintech company and a Director of Data Science at a health tech company in Fort Lauderdale. See Head of Data & AI.
Can we hire data engineers nearshore?
Yes. We recruit engineers across Latin America who work US hours, and pipeline, modeling and platform work fits a nearshore team well because it runs through code review and scheduled on-call. Rates depend on the stack and seniority. See Nearshore.
What is the HEART standard?
HEART is the standard we screen every candidate against, beyond skills: high agency, execution, accountability, resourcefulness and transparency. It describes people who take ownership, turn ideas into results and move the business forward. See the standard.
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