For US product and engineering leaders
Hire Data Engineers for US teams: specialists who build pipelines, warehouses and lakehouses, tested at every step and run in your stack.
- Senior engineering oversight
- US workday overlap
- Your stack and controls
Start with one data engineer. Add a team when the platform needs more capacity.
Data capacity, inside your org.
Data engineers who work in your codebase, quality gates and release rhythm.
Built for product and engineering leadersOne senior data engineer to a full data team
- 01Years engineering software
- 20+
- 02Engineers under one roof
- 200+
- 03Clients served
- 300+
- 04Global delivery locations
- 4+
Dallas headquarteredDelivery across the USA, India, Canada and Mexico
Tools hired Data Engineers use
We work with the data engineering tools, frameworks and architecture your teams already use: Spark, Airflow, dbt and Snowflake.
Core
- Apache SparkBatch processing
- Apache AirflowOrchestration
- DBTdbtData modelling
- SnowflakeData warehouse
- Apache KafkaEvent streaming
- DatabricksLakehouse
Ecosystem
- Python
- SQL
- BigQuery
- PostgreSQL
- Apache Flink
- Airbyte
- Trino
- Terraform
- Great Expectations
We choose tools for your product, your data, your security needs and your deployment plan.
Data work we cover
The data engineering work our engineers take on, from pipelines and warehouses through quality tests and cost, and the tools they use every day.
Reliable pipelines
Pipelines that run on time every morning, wait on their dependencies and retry what fails before anyone notices.
Common work
- Pipeline development
- Orchestration
- Backfills
Tools
- Apache Airflow
- Prefect
- Python
- Docker
Data you can trust
Tests at every layer for schemas, nulls, duplicates and row counts, so bad data stops before it reaches a dashboard.
Common work
- Data quality
- Data contracts
- Freshness alerts
Tools
- dbt
- Great Expectations
- Apache Airflow
- SQL
A warehouse the business can use
Raw data modelled into clean, documented tables, from the landing layer to the numbers the board reads.
Common work
- Data modelling
- Warehouse design
- Semantic layers
Tools
- dbt
- Snowflake
- BigQuery
- Looker
Platforms that cost less to run
Batch where it’s enough, streaming where it pays, and compute sized to the job, so the bill stays in check.
Common work
- Cost optimisation
- Streaming
- Platform migration
Tools
- Apache Spark
- Apache Kafka
- Databricks
- Terraform
Why JumpGrowth
Senior India-based engineers for US teams, backed by delivery across the USA, India, Canada and Mexico.
- Years engineering software
- 20+
- Engineers under one roof
- 200+
- Clients served
- 300+
Engineers matched to your work
Start from the data stack, domain, seniority and delivery habits you actually need. Review profiles and test the technical fit before you choose.
- Specialists
- Domain experience
- Direct interviews
Your team keeps ownership
Your leads set priorities, review the work and own the roadmap. Our engineers join your rituals, your repositories and your release process.
- Your tools
- Your rituals
- Your roadmap
Capacity without the handoff
Nothing is thrown over a wall. Engineers work in your sprints with US workday overlap, so pipelines, warehouses and lakehouses move every day.
- US overlap
- Daily stand-ups
- Shared sprints
Data Engineer hiring models
Embed a data engineer in your team, add a small data squad, or pair data engineers with a dedicated delivery pod.
Staff augmentationEmbed one data engineer
A dedicated data specialist in your team, your codebase and your sprint cycle, reporting to your lead.
- Apache Spark
- Apache Airflow
- dbt
Augmented squadAdd a team of data engineers
Several data engineers for a clear gap across pipelines, modelling and the warehouse, led by your own managers.
- Apache Airflow
- dbt
- Snowflake
Managed delivery podPair data with a pod
Data Engineers with a dedicated delivery pod, so pipelines, models and release stay in one team.
- Apache Spark
- Architecture
- Delivery
You stay in control. Our engineers work in your codebase, your tools and your delivery process.
Cost savings calculator
Move the slider to compare a senior US data engineering team with an India-based team of the same size. Price depends on seniority, US overlap and whether you hire one engineer or a pod.
A planning estimate: $90 an hour onshore and $32 offshore, 160 hours per person each month. Not a JumpGrowth quote. Final cost follows seniority, overlap and scope.
US onshore at $90/hr
$43,200
per month
Offshore at $32/hr
$15,360
per month
Estimated monthly savings
$27,840
About 64% lower than the US onshore estimate.
Get a costed plan for the Data Engineer roles, seniority and team shape you need.
Find the right skill
Pick a skill to see the kinds of engineers we match to your team: data architect, senior data engineer, analytics engineer or streaming engineer.
Senior specialty role
Data Architect
Sets the shape of your data platform: sources, layers, storage and the standards the team builds to.
- Snowflake
- Databricks
- dbt
- Terraform
Senior specialty role
Senior Data Engineer
Builds pipelines end to end, from the source system to the modelled table, with the tests alongside.
- Python
- SQL
- Apache Spark
- Apache Airflow
Senior specialty role
Analytics Engineer
Turns raw tables into documented, tested models and metrics your analysts and dashboards rely on.
- dbt
- SQL
- Snowflake
- Looker
Senior specialty role
Streaming Data Engineer
Builds the real-time feeds behind decisions that can’t wait, from event capture to the landed table.
- Apache Kafka
- Apache Flink
- Apache Spark
- Python
Example roles. We confirm availability and fit before you decide.
What our engineers do
Add the skills you need across pipelines, warehouses, streaming and platform ops.
Build
Build data pipelines
Data engineers who build the pipelines, warehouses and lakes your reporting and products depend on.
- Apache Spark
- Apache Airflow
- dbt
Integrate
Connect your sources
Specialists who bring data in from your apps, databases and SaaS tools, in batch or as it happens.
- Apache Kafka
- Airbyte
- APIs
Platform
Run it securely
Engineers who run your data platform in your cloud, with access, cost and freshness under watch.
- Cloud platforms
- Infrastructure as code
- Governance
How they help
Hire one data engineer or a data team. We cover pipelines, warehouses, quality and production.
- 01
Connect your sources
Ingestion from your apps, databases and SaaS tools, in batch or streaming, landing raw data you can replay.
Ingestion, batch and streaming
- 02
Model the warehouse
Layered models that clean, join and sum raw data into tables the business can trust.
Modelling, warehouses and lakes
- 03
Orchestrate and test
Scheduled runs with dependencies, retries and data tests, so nothing bad is published.
Orchestration, tests and quality
- 04
Operate and improve
Freshness, cost and access watched in production, with runbooks and alerts your team owns.
Freshness, cost and governance
Need a related team around your data engineers?
Find the right roleHow teams use us
The roles we brought in, and what each team achieved. Examples of use, not guarantees.
- US product company01
Dedicated product pod
Data platform team
We provided data engineers to stand up a modern data platform and set up reliable nightly pipelines.
Capability mix
- Data Architect
- Senior Data Engineer
- Delivery Lead
Apache Airflow · dbt · Snowflake · Python
- Faster
- time to insight
- Reliable
- nightly runs
- US enterprise platform02
Architecture and delivery
Warehouse modernisation
We added specialists to move the platform’s legacy reporting onto a modern lakehouse and pay down its technical debt.
Capability mix
- Data Architect
- Analytics Engineer
- Platform Engineer
Databricks · Apache Spark · dbt · Terraform
- Modern
- data architecture
- Lower
- running costs
- US financial services03
Augmented squad
Governed financial data
We placed data engineers inside the client’s strict security and compliance workflow to deliver audited reporting pipelines.
Capability mix
- Senior Data Engineer
- Analytics Engineer
- Security Engineer
Apache Kafka · Snowflake · dbt · Apache Airflow
- Compliant
- data pipelines
- Trusted
- reporting
Client details are kept private. Roles show the skills each engagement used. Results are examples, not guarantees. Related reading: Case studies, Hiring insights on the blog, How we build teams and Prototype to launch.
One team. Two locations.
Your leaders own the product and the architecture. Our India-based engineers join your way of working, backed by delivery across the USA, India, Canada and Mexico.
- Start small with the data engineer you need now.
- Work your way in your codebase, tools and quality process.
- Grow when needed by pairing data engineers with a dedicated delivery pod.
- 01Data ArchitectArchitecture
- 02Senior Data EngineerCore delivery
- 03Analytics EngineerModelling
- 04Streaming Data EngineerStreaming
- Rituals
- Daily overlap
- Ownership
- Sprint outcomes
- Start
- Roadmap-led
Is this a good fit?
- 01You need Data Engineers to ship pipelines, warehouses and lakehouses in your stack.
- 02You want to embed Data Engineers in your team or pair engineers with a dedicated delivery pod.
- 03You need engineers who follow your security and release process.
- 04You want India-based engineers who work closely with your US team.
How it works
Leadership context
Discovery call
Tell us about your product, the data gap and whether you need an architect, senior data engineer, analytics engineer or streaming engineer.
What we cover
- Your goals
- The team gap
- Your data context
Team shape
Pick the model
Choose one data engineer, a small data squad, or engineers paired with a delivery pod, and agree how the team works with yours.
What we cover
- Engagement model
- Seniority mix
- Working hours
Shortlist
Meet vetted options
Within days, review data engineers matched to your stack, your domain and the way you deliver.
What we cover
- Matched profiles
- Evidence of skills
- Availability
Your decision
Interview and select
Interview the shortlist yourself, test the technical fit and choose who joins.
What we cover
- Technical interview
- Team fit
- Your final pick
First sprint
Onboard and kick off
Access, introductions and a walkthrough of the codebase, then the first tickets inside your sprint.
What we cover
- Access and tools
- Codebase walkthrough
- First commit
Step 01Data Engineer case studies / 02
Products that move, store and report on data—the work hired Data Engineers own. Open a study for the brief, platform and stack.
Data Engineer client stories / 01
Clients who needed a senior team that could keep data and delivery moving, in their own words.
A steady, senior team that raised the quality bar early and kept it there, release after release.
01How do I hire Data Engineers for my team?
Hire Data Engineers to build pipelines, warehouses and lakehouses in your stack. Embed one engineer in your team or add a data squad. You interview the shortlist and choose who joins.
02What data engineering skills can we hire for?
Data architecture, batch and streaming pipelines, Spark and Kafka, orchestration with Airflow, dbt modelling, Snowflake, BigQuery and Databricks, data quality and cost tuning.
03Do hired Data Engineers use batch or streaming?
Batch, unless a decision needs the data within minutes. Streaming costs more to run and to staff, so we use it where it pays.
04Can hired Data Engineers work in the stack we already have?
Yes. We work in your warehouse, orchestrator and cloud, and change them only when cost or reliability asks for it.
05How do hired Data Engineers stop bad data reaching dashboards?
Tests at every layer: schemas, nulls, duplicates and row counts. A failed check stops the run before anything is published.
06Who owns the pipelines after Data Engineers join?
Your team. Every pipeline is in version control and documented, and handed over with runbooks and alerts already wired.
07Can we embed Data Engineers in our team or hire a pod?
Both. Add one data engineer through staff augmentation, or pair Data Engineers with a dedicated delivery pod when you need pipelines and release in the same team.
08What affects the cost of hiring Data Engineers?
Seniority, US workday overlap, and whether you hire one engineer or a pod. The planner on this page uses a $90/hr onshore and $32/hr offshore planning rate, 160 hours a month. It is an estimate, not a JumpGrowth quote.
09Is this staff augmentation or a managed delivery team?
Both. Add one data engineer, a small team, or a full pod with its own lead.
10Can we start with one data engineer?
Yes. Most teams start with one data engineer in a clear gap and add people once the fit is proven.
11Can we interview data engineers before we choose?
Always. You see the profiles, run your own interviews and make the final call.
12Can we scale the team after the first data engineer starts?
Yes. Add people as the roadmap grows, or move from one data engineer to a managed pod with its own lead.
Hire Data Engineers
Tell us whether you need a data architect, senior data engineer, analytics engineer or streaming engineer, and whether they join your team or a pod.