// hire - data engineering
Hire data engineers who build the foundation AI runs on.
Pipeline and infrastructure engineers who join your team, work against your own systems, and make sure the data feeding your models and dashboards is actually trustworthy.
- Data engineers
- ETL / pipeline engineers
- Data platform engineers
- Data warehouse engineers
- Data reliability engineers
- Data integration specialists
// build your team
Tell us what you're building
Loading form…
definition
What does it mean to hire a data engineer?
A dedicated data engineer is an infrastructure engineer who joins your team on a defined engagement, designs and maintains the pipelines that move your data, and owns reliability outcomes the way an in-house hire would — including the AI/ML and analytics workloads that depend on it.
key takeaways
Syndell's data engineers build pipelines with Apache Kafka and Apache Airflow, warehousing in Snowflake, and transformation logic in Python and SQL.
A recent property-management platform handled by this practice processed 2.5M+ virtual tours through its data pipeline, supporting an 85% rise in successful transactions.
Data engineering pipelines directly feed our AI/ML and data science practices, so a model is only as reliable as the pipeline this hire builds underneath it.
Syndell's engineering studio is based in Ahmedabad, India, serving businesses across the US, UK, Australia, Canada, and UAE, with a guaranteed daily overlap window against your working hours.
// why AI/ML projects stall on data, not models
Most stalled AI/ML initiatives don't fail on the modeling — they fail because the data feeding the model was late, incomplete, or silently wrong. A dedicated data engineer is the hire that prevents that failure mode: pipelines that catch a broken feed before it reaches a dashboard, warehousing that scales past a single analyst's laptop, and monitoring that flags drift before a stakeholder notices the numbers look off.
capabilities - seven
What your data engineering hire can own.
From pipeline design through ongoing reliability — pick the slice you need.
- 01Data infrastructure design
Pipelines and storage architected around your actual data volume and access patterns, not a generic reference architecture.
- 02ETL & ELT pipeline development
Extraction, transformation, and loading built to run reliably on a schedule, not babysat by hand every week.
- 03Real-time data processing
Streaming pipelines for the data that can’t wait for a nightly batch job to catch up.
- 04Data warehouse & lake architecture
Storage designed so the AI/ML and analytics teams downstream can actually query it, not just archive it.
- 05Pipeline automation
Scheduling and orchestration that catches a broken feed before a report goes out with bad numbers.
- 06Data reliability & verification
Monitoring and validation built into the pipeline, so a silent data quality issue doesn’t surface three reports later.
- 07Support & maintenance
Ongoing monitoring and fixes after launch, priced into the engagement instead of a surprise invoice later.
how it works
Four steps to an embedded data engineer.
- 01
Scope the role
Data volume, existing infrastructure, and the outcome the hire owns. One call is usually enough to size it.
- 02
Meet vetted candidates
Pre-screened data engineers from our bench — you interview them exactly like your own hires.
- 03
Trial the fit
A working period where you evaluate a real pipeline build before committing to the full engagement.
- 04
Confirm and embed
The engineer joins your backlog, your standups, and your definition of done, with a guaranteed daily overlap window.
the stack our data engineers work in
- Apache Kafka
- Apache Airflow
- Snowflake
- Apache Spark
- dbt
- Python
- SQL
- AWS
-- the bench you're hiring from --
years of experience
projects delivered
happy clients
countries served
client recommendation
engagement
Which engagement model fits you?
| Dedicated engineer | Hourly engagement | Fixed-scope build | |
|---|---|---|---|
| Best for | Ongoing pipeline and infrastructure work | A defined block of pipeline or migration work | A single pipeline or warehouse build with a clear finish line |
| You get | A named data engineer embedded full-time in your team | Flexible hours against a scoped deliverable | A scoped deliverable with an agreed timeline |
| Timeline | Monthly, renews | Weeks to a few months | Project-length |
No pricing tables here on purpose — data volume, existing infrastructure, and scope drive cost, and a real number after one call beats a misleading one now. Tell us what you're building.
frequently - asked
Five questions,
straight answers.
01What does it cost to hire a dedicated data engineer?
It depends on seniority, engagement model, and the size of the data infrastructure involved. We don't publish rate cards because scope drives cost too much for a number to mean anything on its own — book a call and you'll get a concrete estimate.
02Will my data engineer overlap with my working hours?
Yes. Every embedded data engineer commits to a guaranteed live overlap window with your team every working day, alongside async standups and reporting.
03What's the difference between a data engineer and a data scientist?
A data engineer designs and maintains the pipelines and infrastructure that move and store data reliably. A data scientist uses that data to build models and generate insights. Most AI/ML work needs both — the pipeline has to be solid before a model can trust what it’s fed.
04What tools do your data engineers work in?
Apache Kafka and Apache Airflow for pipeline orchestration, Snowflake and Apache Spark for storage and processing, and Python and SQL for the transformation logic in between.
05How do you handle data security and compliance?
Access controls, encryption, and audit logging are built into the pipeline design from the start, with the specific standards scoped against your industry during onboarding.
build the team
Explore other roles you can hire.
- All developer teams
every role Syndell staffs, from AI/ML to Shopify
- Hire data scientists
the modeling layer built on top of the pipeline this hire ships
- Hire AI/ML developers
production models trained on the data this hire pipelines
- Hire RPA developers
automate the workflows that generate the data your pipeline moves
- Hire Node.js developers
services and APIs around your data platform
- AI consulting services
the practice this hire draws on for use-case scoping and delivery
Comparing agencies before you commit? Browse the work our teams have shipped or read our full services overview.
