Hire in days, not months

Hire Data Engineers

Ship scalable data infrastructure with senior data engineers who ensure data quality and reliability.

From complex ETL pipelines with Airflow to high-performance data warehousing with Snowflake, our data engineers help you build robust data foundations, maintain high data integrity, and keep data delivery predictable.

Get Matched with Data Talent

Data delivery governance

Governance built for stable and secure data releases

Reduce data risk with explicit release controls, security standards, and quality monitoring tailored to enterprise data environments.

Controls teams ask for before data launch

Stability, security, and quality discipline mapped to how modern data stacks actually ship.

Shortlist turnaround

3.8 days median across recent data roles

Kickoff speed

9 days median from selection to sprint start

Data reliability

96% of data pipelines active after 90 days

Data security and privacy protection

Secure data handling, encrypted storage, and compliance with data security standards for all workloads.

Security-ready

Clear IP and data ownership

Full ownership of data pipelines, scripts, and data assets from day one.

Legal-ready

Data quality and health monitoring

Real-time tracking of data quality, pipeline health, and user-perceived performance in production.

Health-focused

Talent pool preview

Vetted Data Engineer profiles ready to interview

Review a balanced shortlist with specialist, senior, and principal depth so you can hire for immediate delivery and long-term technical leadership.

View more profiles

Saad Q.

Senior Data Engineer

Vetted

8 years

Role-matched

PythonAirflowSnowflakedbt

Led the data infrastructure modernization for a high-traffic SaaS platform, implementing Airflow and Snowflake that reduced data processing time by 50%.

Adnan F.

Data Engineer

Vetted

6 years

Role-matched

SQLSparkAWS GlueRedshift

Built a secure and compliant data platform for a fintech firm, implementing real-time data processing and maintaining high data integrity.

Bilal U.

Principal Data Engineer

VettedArchitect

12 years

Role-matched

Data ArchitectureKafkaSecurityCI/CD

Architected a large-scale data platform with multi-team delivery using Kafka and Kubernetes, improving data accessibility and release frequency by 35%.

Need a wider shortlist?

We can share additional data engineer profiles by seniority, timezone, and domain fit.

Data engagement options

Choose the engagement model that matches your data roadmap

Start with focused infrastructure work or scale to a full data pod as your product complexity grows.

Model selection support

We map data role shape to roadmap pressure, technical complexity, and stakeholder expectations.

Part-time data support

Best for iterative pipeline work, cost optimization, and ongoing maintenance.

Starts from $2,000 / month

Best for: Steady data improvements and maintenance

20-25 hrs/week
Data sprint support
Weekly cost reporting

Large-scale data migrations and security audits are scoped separately.

Get Started

Full-time data engineer

Recommended

Best for core data feature delivery with daily ownership and production momentum.

Starts from $4,000 / month ($25/hour)

Best for: Active data roadmap execution and product integration

40 hrs/week
Full ownership
Daily cost + progress updates

Cloud data platform costs and third-party licensing are billed separately.

Get Started

Data engineering pod (2 Data Devs + 1 Data Scientist + 1 PM)

Best for new product launches, major data sets, and cross-functional execution.

Starts from $12,000 / month

Best for: High-stakes initiatives with significant coordination needs

Cross-functional data pod
Parallel data workstreams
End-to-end orchestration

Specialized security audits are scoped separately.

Get Started

Data hiring process

From data roadmap to production contribution in under two weeks

The process is tuned for data delivery risk: architecture fit, pipeline depth, and release reliability.

Typical kickoff window

Most teams start data delivery with selected talent in 7-14 days.

Decision points are explicit: data implementation depth, quality discipline, and communication quality are validated before kickoff.

1
Data goal alignment and scope
Step 1
We map your data objectives, technical requirements, and budget goals to define role scope and success metrics.
Day 1-2
2
Shortlist with relevant data context
Step 2
Review candidates with prior experience in similar data domains, architecture patterns, or scale constraints.
Day 2-5
3
Technical validation with data scenarios
Step 3
Interviews test data implementation logic, pipeline depth, and data-specific tradeoff handling.
Day 5-10
4
Onboarding and data sprint integration
Step 4
Selected engineers join your workflows with clear ownership and immediate first-sprint goals.
Day 7-14

Why product teams hire us for data

Data execution tuned for scale, security, and operational reliability

You get engineers who can build production-grade data systems without the overhead of a traditional data team.

Built for high-stakes data delivery

Designed for teams shipping SaaS products, ecommerce tools, and performance-critical data experiences.

Typical start

9 days median to sprint kickoff

Quality impact

30% median reduction in data errors

Pipeline speed

35% increase in data delivery frequency

Fast ramp on data projects

Engineers integrate into your architecture, pipeline patterns, and release flow quickly.

Velocity

Focus on data stability and security

Engineers prioritize reliability and security to ensure a high-quality user experience.

Reliability

Cost-optimized data performance

Delivery decisions account for scale, speed, and efficient resource utilization.

Performance

Service scope

Data engineering use cases mapped to business outcomes, not just pipelines

Use this service scope to match your data roadmap to the right implementation pattern, whether you need a new data platform, pipeline expansion, or cost optimization.

Data Infrastructure and Pipelines

Scalable ETL/ELT pipeline development

Our data engineers build robust, automated pipelines using Airflow, Spark, or dbt to ingest, transform, and load data from diverse sources into your data warehouse.

Data warehousing and lakehouse architecture

Hire data experts to design and implement scalable data storage solutions using Snowflake, BigQuery, or Databricks, ensuring high performance and data accessibility.

Real-time data streaming and processing

Design and implement real-time data pipelines using Kafka or AWS Kinesis for immediate insights and event-driven data processing.

Data Quality and Governance

Data modeling and schema design

Create efficient, scalable data models and schemas that support complex analytics and reporting requirements while ensuring data integrity.

Automated data quality and validation

Implement automated checks and validation layers to catch data issues early, ensuring your analytics are based on accurate and reliable data.

Data security and compliance

Design secure data architectures with encryption, access controls, and compliance best practices to protect sensitive information and meet regulatory standards.

Analytics and Optimization

Performance tuning and query optimization

Hire data engineers to identify and fix data processing bottlenecks, optimizing SQL queries and pipeline performance to reduce latency and cost.

Data integration and API development

Build secure data APIs and integration layers that allow your applications and stakeholders to access data easily and reliably.

Cloud data cost optimization

Identify and fix inefficiencies in your cloud data spend, using monitoring and cost management tools to keep your data budget on track.

Engineering stack

Production-ready data stack for scale, security, and cost efficiency

Stack choices are optimized for fast iteration, high availability, and long-term maintainability across modern data products.

Apache Spark

Python

SQL

dbt

Apache Airflow

Prefect

Dagster

Snowflake

Google BigQuery

Amazon Redshift

Databricks

Apache Kafka

Amazon Kinesis

RabbitMQ

AWS Glue

Azure Data Factory

Docker

Kubernetes

Hiring readiness

Data hiring playbook: evaluate quickly and onboard with less risk

Use this decision hub to align data interview depth, set quality boundaries, and connect hiring to measurable outcomes.

Role scope Interview playbook Business impact

Responsibilities / Role Scope

Owns

Data pipeline implementation with high reliability and quality standards
Data modeling and schema design for scalable analytics
Data cost optimization and performance monitoring
Security and compliance implementation for data workloads

Collaborates on

Product teams to define data roadmap and service feasibility
Data scientists for efficient data access and feature engineering
DevOps teams for secure deployment and release orchestration
Security teams for risk assessment and compliance audits

Interview Questions

Structured by level for consistent and faster interviewer calibration.

junior

Fundamentals and execution reliability

What is the difference between ETL and ELT?
What are the core components of a data warehouse?
How do you write a basic SQL query to join two tables?
What is the purpose of a primary key in a database?

mid

Delivery ownership and decision quality

How do you handle data quality issues in an automated pipeline?
What are the benefits of using a columnar storage format like Parquet?
How do you implement incremental data loading in a data warehouse?
How do you optimize Spark jobs for better performance?
How do you handle schema evolution in a data lake?

senior

Architecture, risk control, and leadership

How do you architect a scalable data platform for a multi-team organization?
How do you design a secure and compliant data architecture for sensitive information?
How do you approach a large-scale data migration with zero downtime?
How do you define and enforce data governance policies across multiple data sources?
How would you optimize a complex real-time data processing pipeline for both latency and cost?

Why Outsource This Role

Faster data delivery

Ship data-driven features without the overhead of local hiring or complex infrastructure management.

Median kickoff: 9 days from role approval

Predictable data spend

Scale your data engineering bandwidth based on active priorities at a predictable hourly rate.

Starts from $25/hour for data engineering

High data integrity

Improve data quality and maintain high reliability with engineers who know data tradeoffs.

Median data accuracy lift: 30% in 12 weeks

Lower data risk

Use data best practices and automated pipelines to reduce security incidents and release delays.

Data processing costs reduced 20% quarter-over-quarter

Scalable data teams

Start with one data engineer and expand to a full data pod as product complexity grows.

Data pod scale-up window: 2-3 weeks

Benefit	Outcome	Metric
Faster data delivery	Ship data-driven features without the overhead of local hiring or complex infrastructure management.	Median kickoff: 9 days from role approval
Predictable data spend	Scale your data engineering bandwidth based on active priorities at a predictable hourly rate.	Starts from $25/hour for data engineering
High data integrity	Improve data quality and maintain high reliability with engineers who know data tradeoffs.	Median data accuracy lift: 30% in 12 weeks
Lower data risk	Use data best practices and automated pipelines to reduce security incidents and release delays.	Data processing costs reduced 20% quarter-over-quarter
Scalable data teams	Start with one data engineer and expand to a full data pod as product complexity grows.	Data pod scale-up window: 2-3 weeks

Client stories

Trusted by teams that ship fast

Real feedback from partnerships where we embedded with product teams, accelerated delivery, and stayed accountable to outcomes.

“What stood out was how quickly they understood both our codebase and business constraints. Their developer contributed meaningful pull requests in week one, improved our testing discipline, and proactively flagged architecture risks before they became expensive problems. It felt less like hiring a contractor and more like adding a senior teammate.”

Elena M.

VP Engineering, Fintech Platform

“Our biggest concern was scalability during a period of rapid growth, and their team handled it with confidence. They refactored key backend services, introduced safer deployment practices, and helped us scale traffic without downtime during peak usage windows. We saw immediate performance gains and far fewer late-night incidents.”

Sarah K.

Engineering Manager, Enterprise Platform

“We needed to scale delivery capacity quickly but were not ready for several full-time hires. Codexty gave us immediate access to vetted talent that integrated into our workflows with minimal ramp-up time. We expanded engineering output while keeping hiring risk and operational overhead under control.”

Chris B.

VP Engineering, Fintech

FAQ

Answers to practical decision questions before you hire.

How quickly can a data engineer start?

Most data projects begin onboarding within 7-14 days after role alignment and interview completion.

Do you work with Spark and Snowflake?

Yes. We specialize in modern data engineering using Spark, Snowflake, Airflow, dbt, and various cloud-native tools.

Can you help with data cost optimization?

Yes. We support identifying and fixing data processing inefficiencies, using data best practices to keep your budget on track.

How do you handle data security?

We use data-native encryption, access controls, and compliance best practices to ensure your data environment is secure and compliant.

What is the hourly rate for data engineers?

Our data engineering services start at $25/hour, providing high-quality data delivery at a competitive rate.