Build a Scalable Data Foundation That Powers AI, Automation & Growth
Hire expert data engineers to build the scalable AI-ready infrastructure that powers your business growth.
We are a data-driven software development company with 100+ engineers specializing in data engineering, workflow automation, AI integrations, and multi-agent systems. We partner with growing and mature companies that need reliable data infrastructure but don’t have in-house Data/AI expertise. Whether you need to hire data engineers for a specific project or seek experts for long-term staff augmentation, we deliver results.
PARTNER
PARTNER
FEATURED IN
.svg)
Key Facts & Results
100+
skilled software engineers across AI, Data, and Web
18+
years of hands-on experience in Data Engineering
92%
client retention rate through long-term partnerships
55+
successful data engineering solutions delivered globally
Why Us
100+ Engineers: Reliable, senior-level talent bench.
15+ Years: Of designing scalable data platforms. We know how to avoid costly architectural mistakes.
Experience: Across data-heavy products and deep expertise in operational environments.
Lifecycle Ownership: Discovery → Architecture → Development → Deployment → Support.
Long-term Partnership: We stay as your external Data & AI team if needed.
When you look to hire experienced data engineer professionals, you need certainty. At DATAFOREST, every senior data engineer for hire has passed rigorous technical vetting to ensure they can navigate complex enterprise environments from Day 1.
What Our Data Engineers Build
01
Data Pipelines & ETL/ELT Systems
We engineer scalable ETL/ELT workflows for automated data ingestion, transformation, and orchestration from multiple sources, APIs, databases, and SaaS tools into your warehouse. Includes monitoring, retry logic, and data quality checks.
02
Data Warehouse / Lakehouse Architecture
Design and implement modern data warehouse solutions with optimized schemas and cloud-native data pipelines. We focus on query performance tuning, partitioning strategies, and cost management for Snowflake, BigQuery, or Redshift.
03
Real-Time Data Streaming & Event Processing
Our real-time data processing engineers build streaming pipelines for analytics and operational intelligence, processing millions of events daily with low latency using Kafka, Kinesis, or Flink.
04
Data Integration & API Development
We integrate data from multiple sources via custom connectors for third-party systems, REST/GraphQL APIs for data access, and webhook handlers to ensure seamless integration with your existing tech stack.
05
Data Quality & Observability Frameworks
Implement data reliability and observability engineering to automate validation, profiling, lineage tracking, and anomaly detection. We catch data issues before they impact analytics or ML models.
06
ML Data Infrastructure & Feature Stores
We build feature pipelines and implement feature store data pipelines for consistent model training/serving, version datasets, and Machine Learning data preparation to provide production-ready data for Data Scientists.
07
Readiness & Agent Enablement
Prepare data for LLMs and multi-agent systems, enabling copilots, automation, and real-time decisioning.
Ready to Build a Data Foundation That Scales?
Book a strategy call — we'll review your systems, identify gaps, and propose the most efficient path forward.
Challenges that DATAFOREST helps solving
.svg)
Internal hiring takes 2–4 months, but you need results now
Spin-up in days, not months—full-time embedded engineer without HR overhead. Find a data engineer for hire immediately.
.svg)
Your team is blocked due to a shortage of data engineering capacity
Utilize data engineering staff augmentation to unblock critical path items.
.svg)
Existing data pipelines are unstable, but no one has time to refactor them
We stabilize and optimize legacy code while your core team focuses on features.
.svg)
No Senior Oversight or Guardrails
Develops predictive mathematical models to forecast potential business scenarios and support strategic planning.
Need Elastic Capacity, Not Headcount
Scale up or down quarterly and access vetted data engineers for hire without committing to permanent roles.
Data growth outpaces current infrastructure
We re-architect for scale, handling increased volume without performance degradation.
Fragmented Data Across SaaS & APIs
Unify CRM/ERP/marketing data into a single, trusted warehouse/lakehouse.
.svg)
Manual Reporting Slows Decisions
Automate ingestion & transformation; ship live, reliable dashboards.
AI and Automation Projects Fail Due to Poor Data
You want to use LLMs, AI copilots, or predictive analytics — but data is unstructured or incomplete.
Outcomes - What Companies Achieve with a Dedicated Data Engineer
Consolidated 21 data sources into a Databricks Lakehouse. We delivered data engineering consulting support that resulted in a Medallion Architecture, reducing annual compute costs by ~50% and ensuring HIPAA compliance.
Summary of Impact
Automated data pipelines replaced spreadsheet-based reporting, freeing analysts for strategic work.
↓ 80% manual reporting workload
Created a single, trusted data layer connecting CRMs, ERPs, and marketing platforms.
20+ SaaS and internal systems unified
Delivered optimized ETL and query logic, cutting insight delivery time from days to minutes.
Days → Minutes analysis cycle
Enabled leadership to track performance instantly with live, interactive BI dashboards.
Real-time executive dashboards
Prepared structured, documented datasets for AI copilots, predictive models, and agentic workflows.
AI-ready data foundation
Our Data Engineering Stack
We specialize in cost-optimized cloud data architecture. If you need to hire big data engineer talent, we have experts in these specific stacks.
Cloud & Data:
Pipelines & Orchestration:
Real-Time Processing:
Visualization & BI:
Governance & Security:
Process Steps
We make the hiring process quick and easy. Describe the skills you need, and we will assign a qualified data engineer to your team within 48 hours.
Requirements & Environment Review
We analyze your current infrastructure and define the scope.
01
Engineer Selection & Technical Interview
You interview the candidates we select to ensure a culture and skill fit. This is how to hire a data engineer without the guesswork.
02
Onboarding & Access Setup
Seamless integration into your security protocols and communication channels.
03
Weekly Delivery & Reporting Cadence
Transparent sprint planning and execution.
04
Articles That Encourage Hiring Databricks Developers
All publications
May 2, 2025
9 min
Best Data Engineering Company: Expert Building Data Architectures
March 25, 2025
13 min
Data Engineering Methods Make Data Automation Intelligent
December 15, 2023
8 min
Mastering ETL and Data Warehousing Insights
FAQ
How quickly can I hire a remote data engineer?
Typically, we can provide a candidate for review within 3–5 business days. Once selected, onboarding usually takes less than a week. If you need to hire remote data engineer talent fast, our pre-vetted bench allows for rapid deployment.
Can we scale the number of engineers up or down?
Yes, flexibility is a core part of our engagement models. You can hire mid-level data engineer staff for routine maintenance or scale up with seniors for complex architecture phases, adjusting as your project evolves.
Do you follow documentation standards and coding conventions?
Absolutely. Whether you decide to data engineer hire via staff augmentation or on a project basis, we adhere to strict CI/CD pipelines, code reviews, and documentation standards, or adapt to yours to ensure the code is maintainable.
What are the typical rates for a data engineer to hire?
Rates vary based on seniority and tech stack. When looking for a data engineer to hire, providing clear requirements allows us to give a precise estimate. Contact us for a detailed pricing sheet.
Can I hire data engineer developer profiles specifically?
Yes. We distinguish between architects and developers. You can hire data engineer developer roles focused on coding pipelines, Python scripts, and API integrations, versus architects who design the high-level system.
How do you ensure quality when we hire data engineer developers?
We use a rigorous internal vetting process, including live coding challenges and architecture design sessions. When you hire data engineers developers from DATAFOREST, they are backed by our internal CTO office and Center of Excellence.
How do you handle communication with remote engineers?
We integrate directly into your Slack, Teams, and Jira/Asana. Our engineers work in your time zone overlap to ensure real-time collaboration and attend your daily stand-ups.
Do you provide ongoing support after delivery?
Yes, we offer ongoing maintenance and support packages to ensure your data infrastructure remains performant and cost-effective after the initial build.
How do you protect data privacy?
We operate under strict NDA and compliance frameworks (GDPR, HIPAA, SOC2 where applicable). We work within your VPC/Cloud environment, meaning data never leaves your controlled infrastructure.
Can you integrate AI with our data?
Yes, this is our specialty. We prepare your data layer (ETL, cleaning, vectorization) to be AI-ready for LLMs, RAG architectures, and predictive models.
Let’s discuss your project
Share project details, like scope or challenges. We'll review and follow up with next steps.
All servicesGenerative AIWeb Scraping ServicesData ScienceData EngineeringWeb And Mobile Development
DevOps & Cloud SolutionsDigital TransformationCustom Software Development
DevOps & Cloud SolutionsDigital TransformationCustom Software DevelopmentSolutions
Advanced Planning SystemsWarehouse Automation SolutionsAgentic AI for Financial AdvisorsData Management & Analytics for Financial AdvisoryCustomer Data Platforms for UtilitiesRetail Customer Data Platform DevelopmentDecision Support SystemCustom E-Commerce PortalClinical Information SystemAI Voice AgentCDP-CRM Data IntegrationDatabricks ArchitectureIndustries
E-commerceRetailTravelTechInsuranceFinanceHealthcareFinTechUtilitiesAI for ManufacturingReal EstateDigital Marketing Analytics
