Reports stop being trusted when the pipelines behind them fail silently and nobody can say where a number came from.
Hire Data Engineers for Pipelines, Lakehouses & Streaming Data
Data engineers who build pipelines that can be rerun safely, model data so that analysts can use it, and add the checks that catch bad records before they reach a dashboard.
- SQL and Python as working languages
- Batch and streaming pipelines run in production
- Data quality tests built into the pipeline
- You interview the engineer before any contract is signed
- DaysTo a shortlist
- MonthlyContract terms
- YoursCode and IP
Teams That Build With SyntecHire
References available on request.
What You Can Build With Data Engineering
Batch Data Pipelines
Scheduled ingestion and transformation with retries, backfills and alerts when a run fails or arrives late.
Tech Stack
- Apache Airflow
- dbt
- Python
- SQL
Outcome
The morning numbers are there when people arrive, and someone is told when they are not.
Apache Kafka, Apache Spark, Apache Airflow and Apache Iceberg are open-source projects governed by the Apache Software Foundation.
Technical Expertise Our Data Engineers Bring
Models that reflect how the business counts things, with grain and keys stated plainly.
- SQL
- Dimensional Modelling
- dbt
Readable, tested pipeline code, packaged so that it runs the same on a laptop and in production.
- Python
- PySpark
- pandas
Dependencies, retries and backfills defined in code, with tasks that are safe to run twice.
- Apache Airflow
- Dagster
- Prefect
Partitioning, joins and file sizes tuned by reading query plans, not by adding machines.
- Apache Spark
- Trino
- Databricks
Topics, schemas and consumer groups designed for ordering, replay and late-arriving events.
- Apache Kafka
- Apache Flink
- Amazon Kinesis
Schema evolution, compaction and snapshot retention managed so that tables stay fast and affordable.
- Apache Iceberg
- Delta Lake
- Parquet
Checks on freshness, volume and validity that stop a bad load before it spreads downstream.
- dbt Tests
- Great Expectations
- Data Contracts
Personal data identified and masked, access granted by role and lineage recorded.
- Access Control
- Data Catalogue
- OpenLineage
How Data Engineering Has Evolved
- 2006
Apache HadoopDistributed storage
Hadoop made it practical to store and process large datasets on clusters of ordinary servers.
- 2011
Apache KafkaEvent streams
LinkedIn open-sourced Kafka, a distributed log for moving events between systems.
- 2014
Apache SparkIn-memory processing
Spark became a top-level Apache project and reached its 1.0 release.
- 2015
Apache AirflowOrchestration
Airbnb open-sourced Airflow, which defines workflows as Python code.
- 2019
Delta LakeTable formats
Databricks open-sourced Delta Lake, adding transactions to data stored in files.
- 2020
Apache IcebergOpen tables
Iceberg, first built at Netflix, graduated to a top-level Apache project.
When Data Engineering Is The Right Choice
Choose Data Engineering when
- Reports disagree with each otherShared, tested models give every team the same definitions.
- Analysts spend their time cleaning dataAn engineer moves that work into pipelines, so that it is done once and done the same way.
- Data arrives from many systemsIngestion, matching and history need design when sources multiply.
- You are preparing for machine learning or AI workModels depend on data that is complete, current and documented.
Consider something else when
- Your data fits in one databaseIf a few SQL queries answer your questions, a read replica and a reporting tool are enough.
- You need analysis, not plumbingQuestions about what the numbers mean belong to an analyst or a data scientist.
- Nobody has decided what to measurePipelines built before the questions are known tend to be rebuilt. Agree the metrics first.
Why CTOs Choose Us
Candidates review existing pipeline code and a data model, then explain where it would fail and how they would fix it.
You meet the person and test their reasoning on your own data problems before any contract.
The engineer works for a single client, so they learn your sources, your definitions and their quirks.
Pipelines are built in your cloud accounts and repositories, under the access you grant.
Month-to-month terms with no exit fee, and a replacement if the fit is wrong.
How Your Developer Joins The Team
- 1Step 1
Discovery
A call about your stack, team and the work ahead.
- 2Step 2
Matching
A shortlist of people who have done similar work.
- 3Step 3
Onboarding
Access, environment setup and a first small task.
- 4Step 4
Shipping
Regular pull requests inside your review process.
AI In Delivery
Where coding assistants help, and where a person decides.
Used for speed
Boilerplate, test scaffolding and first drafts of documentation.
Always reviewed
Every change is read and approved by an engineer before it merges.
Never given secrets
Credentials and client data stay out of prompts. Your policy on AI tools applies.
Security And IP
What we enforce on every engagement.
IP assignment
All work product is assigned to you in the contract.
NDA first
Signed before anyone sees your code.
Least privilege
Access limited to what the task needs.
Clean exit
Access revoked and handover documented when work ends.
Data Engineer Pricing Tiers
- Early career
Entry
On request
- Works on defined tasks
- Pairs with a senior reviewer
- Suited to well-scoped backlog items
- Mid level
Experienced
On request
- Owns features end to end
- Reviews others' code
- Suited to most product teams
- Senior
Expert
On request
- Leads architecture decisions
- Mentors the team
- Suited to complex or legacy systems
Rates depend on seniority, stack and team size. We quote a fixed monthly figure per developer.
What Is Included In The Rate
| SyntecHire dedicated developer | Typical freelancer | |
|---|---|---|
| Screening and onboarding | Included | Your time |
| Replacement if the fit is wrong | Included | Start again |
| Continuity and handover notes | Included | Varies |
| IP assignment and NDA | In the contract | Varies |
| Availability | Full time on your product | Shared across clients |
Is A Dedicated Developer The Right Fit?
Five quick questions.
1Is the work expected to run for three months or longer?
2Do you have someone who can set priorities each week?
3Is there an existing codebase or a clear specification?
4Do you want the developer inside your own tools and reviews?
5Would losing context between contractors hurt the project?
0 of 5 answered. Answer every question to see the result.
Related Insights
View All Articles- HiringHow to brief a remote developer so week one is productiveA short checklist covering access, context and a first task that is small enough to finish.
- Project ManagementStaff augmentation or a dedicated team: a decision you can make in ten minutesFour questions about ownership, runway and management time that settle the choice.
- ModernisationSigns your legacy platform has become a business riskWhat to look for in release frequency, incident history and hiring difficulty.
Data Engineering Hiring Questions
Tell us your cloud, warehouse and orchestration tools. We shortlist engineers who have run those in production, and you confirm the match in the interview.
Batch is enough for most reporting. Streaming earns its cost when a decision has to be made within moments of an event. The engineer can review each use case and recommend one, with reasons.
An NDA is signed before any access. The engineer works inside your environment with the permissions you grant, and you decide whether development uses masked or sample data.
Many can build a basic dashboard, but their main work is the data underneath. If reporting design is the priority, tell us and we take it into account in the shortlist.
Yes. The usual start is to map what runs, what depends on it and what fails most often. Fixes and documentation follow in that order.
You do. All code is written in your repositories, and the contract assigns the work and intellectual property to you.