Data Engineering & AI Development Services

Hire Specialists for Production Work

TechBar provides engineers working inside your existing setup in 1-2 weeks and owning data systems in production — even when data is messy and constantly changing.

How It Works

Why Choose TechBar for AI & Data Engineering Services

The product keeps shipping, but the data side starts to lag — a common reality for many businesses. At this point, it becomes clear that your team can’t go further without fixing the issues, and, at the same time, no one owns the task. That’s when businesses usually hire data engineers and AI specialists at TechBar.

Our developers join your team and take over progress blockers. Plus, these specialists quickly pick up the work and move it forward without extra coordination from your side, freeing your leadership to focus on other priorities while we deliver forward momentum.

Most importantly, TechBar adapts to your stack and follows the way your team already builds and releases. We provide first engineer profiles within 2-3 business days, faster than most competitors, letting you see results with minimal wait.

When You Need Data & AI Engineers

You have backend developers, but no one owns the data layer, so pipelines get added ad hoc and break under load.
You plan AI features, but models don’t make it into the product.
One person owns all data scope, tasks queue up around them, and delivery slows down.
You define the data platform, but ingestion, transformation, and storage don’t progress.
You need to migrate or rebuild your data setup, but you can’t risk breaking what already runs in production.
You want to add LLM or AI features, but the data layer isn’t ready, and the system’s components don’t come together into an operating solution.

What we build

Legacy Modernization Strategies:
How We Choose the Right Approach

ETL / ELT Pipelines

Batch and real-time ingestion from multiple sources as part of ETL pipeline development, with monitoring, retry logic, and data quality checks built in. Pipelines run in production without manual intervention.

Data Warehouse & Lakehouse

TechBar delivers data warehouse services on Snowflake, Databricks, and BigQuery. We set up access controls, organize data with medallion layers, and tune queries for predictable performance.

BI & Self-Serve Analytics

Dashboards and semantic layers in Power BI and Looker replace manual reporting. Our clients can use consistent metrics and access data directly, requesting support from developers only when needed.

Real-Time Streaming

Kafka data engineers at TechBar build real-time streaming data pipelines for event-driven systems. These pipelines process high-throughput data and keep streams consistent under load.

ML Model Deployment

Production processes include ML model deployment with defined MLOps services. Our developers set up monitoring, versioning, and retraining.

AI / LLM Integration

LLM integration services start with data preparation and RAG pipeline development. TechBar connects LLM features to existing services so they run inside the product with real inputs.

Data Migration

Migration covers moving data from legacy systems to cloud platforms without downtime. TechBar, as a data engineering company, runs dual-write flows, reconciliation checks, and rollback control.

Data Governance & Quality

Data governance services focus on contracts, lineage tracking, and quality checks. We apply GDPR, HIPAA, and SOC2 requirements directly in the data architecture.

How we work

Our Structured Engagement Framework

Five stages with clear deliverables at each step. The framework reduces uncertainty from the first conversation through ongoing delivery.

Understanding Discovery & Needs Analysis
We assess your current architecture, delivery process, project requirements, and engineering capacity. The goal is to clarify the engagement approach before candidate sourcing begins.

Deliverable: Consultation with our Solutions Architects outlining technical requirements and the recommended engagement model.

Discovery & Needs Analysis
Strategy Team Structure & Skill Planning
We determine the team’s roles, seniority, and domain expertise based on the project goals and talent augmentation needs. This structure ensures the specialists can start delivering without lengthy ramp-up.

Deliverable: Engagement proposal with team structure, timeline, and cost model.

Team Structure & Skill Planning
Quality Candidate Selection & Technical Vetting
Each candidate undergoes a multi-stage evaluation: technical assessment aligned with your stack, a live problem-solving session, and a review of communication and domain fit. Only pre-qualified profiles reach your team, and the hiring decision remains entirely yours.

Deliverable: Pre-qualified candidate profiles with assessment summaries and availability.

Candidate Selection & Technical Vetting
Integration Onboarding & First Delivery
Engineers join your development environment and delivery workflows as part of the outstaffing model. Within five business days, onboarding covers codebase orientation, an architectural walkthrough, and the first delivered task.

Deliverable: First-week alignment review between your engineering lead and our Delivery Manager.

Onboarding & First Delivery
Partnership Ongoing Delivery & Collaboration
After onboarding, we run regular performance assessments and review delivery progress with your team. Clear escalation paths and defined SLAs help us respond to problems quickly, including engineer replacement if needed.

Deliverable: Dedicated account management, monthly reviews, and direct contact with senior leadership for urgent issues.

Ongoing Delivery & Collaboration

Advantages

What Changes When TechBar
Engineers Join Your Team

Pipelines and Models Remain Stable

TechBar provides data pipeline services that run on live software. When data changes or a job fails, pipelines continue processing and recover without manual fixes.

Engineers Join in 1–2 Weeks

You receive matching profiles within 48–72 hours. Selected specialists are onboarded by the next sprint planning and start with tasks already in progress.

One Team Covers the Data Flow

Our engineers take data from ingestion through model deployment and dashboards as one stream. The same specialists move pipelines end to end, without gaps between teams or handoffs.

The Project Continues in Your Stack

TechBar experts operate within projects on your existing stack, using the tools already in place. The setup stays the same, and we select experts to match it.

LLM Features Plug into Your Existing Tools

We connect LLM features to your current services and data. You get chatbots and RAG-based workflows that run inside the product and use company information, without building a separate data layer first.

All Tasks Stay in Your Tools and Process

TechBar developers use your Jira, commit to your repositories, and join standups. At the same time, tasks move through your SDLC with IP and NDA in place.

Available expertise

Data & AI Engineers Ready to Join Your Team

Anonymized profiles from our current bench. These specialists are available now and have experience in data pipelines, warehouses, and AI development services.

Yurii L. Senior Data Engineer

7+ years
EU
Available now

Built scalable data platform processing 1TB+ of data daily for a global e-commerce company. Reduced data pipeline latency by 60% and enabled near real-time analytics, accelerating business decision-making across multiple teams.

Airflow
dbt
Python
Snowflake
Spark

Ivan T. AI / NLP Specialist

5+ years
EU
Available now

Built NLP-powered document analysis system processing 100k+ legal documents monthly. Reduced manual review effort by 70% and improved information extraction accuracy using LLM-based pipelines.

Hugging Face
LangChain
OpenAI API
Pinecone
Python

Victor A. Senior DevOps / SRE Engineer

6+ years
EU
Available now

Developed and deployed ML models for fraud detection processing 5M+ transactions daily. Improved fraud detection accuracy by 28% and reduced false positives by 35%, directly impacting revenue protection.

FastAPI
MLflow
Python
SageMaker
TensorFlow

Dima N. BI / Analytics Engineer

4+ years
EU
Available now

Developed BI infrastructure and dashboards used by 200+ business users across departments. Reduced reporting time from hours to minutes and enabled real-time visibility into sales and operations metrics.

BigQuery
dbt
Looker
Power BI
SQL

Michael A. Data Architect

9+ years
EU
Available in 1 week

Designed enterprise data architecture across 15+ data sources for a SaaS platform with 100k+ users. Reduced data duplication by 40% and improved reporting consistency, enabling reliable analytics at scale.

Azure
Databricks
Delta Lake
SQL
Terraform

Vlad M. Streaming / Real-Time Engineer

6+ years
EU
Available in 2 weeks

Designed real-time data pipelines processing 2M+ events per minute for an ad-tech platform. Reduced processing latency to under 2 seconds and enabled real-time personalization and bidding optimization.

ClickHouse
Flink
Kafka
Kubernetes
Python

Technology depth

Data & AI Technologies Our
Engineers Work With Daily

Apache Spark Processing
Airflow Orchestration
dbt Transform
Kafka Streaming
Snowflake Warehouse
Databricks Lakehouse
BigQuery GCP Analytics
TensorFlow ML Framework
PyTorch ML Framework
S3 · Glue · Redshift AWS Stack
Synapse · ADF Azure Stack
LangChain Agents · LLM
LangGraph Agents
PostgreSQL Databases
Looker Visualization
Power BI Visualization
Tableau Visualization
PgVector Vector Storage
Pinecone Vector Storage
Delta Lake Platforms
Docker / K8s Infrastructure

Know more about engineering tech stack

Engagement models

Staff Augmentation, Dedicated Teams, and Centers of Excellence

Not every engineering challenge requires the same setup. Some projects need one senior engineer on short notice. Others require a full capability center. TechBar structures the engagement to match your scope.

Staff Augmentation Most popular
As a staff augmentation company, TechBar provides senior developers for hire who join existing processes and start contributing quickly. You manage priorities and delivery while we provide the talent and support.
- First candidate profiles within 48–72 hours, onboarding in 1–2 weeks.
- European software engineers work in your tools, attend your standups, and follow your sprint cadence.
- Scale from one engineer to a multi-discipline group as your workload grows through software team augmentation.
- Urgent staffing or fast replacement when a developer leaves the project.
Best for: Tech leads and VPs of Engineering who know the skills they need and want to expand their engineering capacity.
Staff Augmentation
Dedicated Development Team A cross-functional team focused on your product
TechBar assembles a stable group of engineers around a specific initiative. The dedicated team model allows engineers to work together long enough to build shared context and product knowledge.
- Cross-functional experts covering backend, frontend, QA, DevOps, and other roles.
- Shared planning with your product leadership and regular sprint rituals.
- TechBar manages hiring, performance, and team growth.
- Flexible composition as the product scope evolves.
Best for: Product companies running long initiatives that require a cohesive team working on a shared roadmap.
Dedicated Development Team
Center of Excellence A dedicated engineering hub for your domain
TechBar establishes and operates a permanent engineering unit aligned with your product roadmap. The specialists develop deep domain expertise and become a long-term extension of your organization.
- A software engineering team developing deep architectural and platform knowledge over time.
- Your SDLC, governance, and security policies guide how the hub operates.
- Knowledge management through shared repositories, documentation, and internal practices.
- Stable teams that preserve expertise as the platform evolves.
Best for: Companies building long-term R&D capability, platform engineering groups, or specialized data and AI teams.
Center of Excellence
Talent Transfer Engineers you can transition to your team
Begin with a TechBar-managed group of developers working on your product. After a defined period, you can hire software engineers directly into your organization.
- Specialists start under TechBar while you evaluate skills and culture fit in real work.
- Transparent buy-out terms agreed upfront.
- Grow your internal team gradually as your product expands.
- Full knowledge transfer, including documentation and architecture context.
- Option to keep TechBar involved for backfill or additional support.
Best for: Companies that want to validate engineers in production and build their internal team step by step.
Talent Transfer

Client testimonials

What Clients Say About Our Engineering Teams

Verified reviews from — 5.0 overall rating across all engagements.

Techbar developed an AI-powered sales automation ecosystem for a business acceleration services company, including a revenue growth engine and agentic AI sales assistants. The solution improved visibility into pipeline and conversion performance, reduced manual effort in sales workflows, and enabled faster lead handling and follow-ups across multiple channels. It also established a scalable foundation for future voice integrations and further automation.

Mark Wilson — Co-Founder, FEtch, AI Development · IT Staff Augmentation · Software Development

The goal was to build an AI-assisted career guidance platform for a career services company. The Techbar team delivered a solution combining a structured multi-session assessment with a database of over 4,000 careers. It includes a recommendation engine, career clustering, and personalized summaries based on user skills, interests, and preferences, along with CV guidance and a public-facing platform with authentication and optional 1-to-1 session booking with the founder.

Kay Tien — Founder, Pivot Pointe AI Development · BI & Big Data Consulting & SI · Software Development

The project involved developing a website for a mergers and acquisitions firm, based on an approved design concept. Techbar delivered a solution aligned with the client’s global M&A advisory positioning, including responsive front-end implementation and CMS configuration for easy content management across offices and service pages. The website was optimized for performance, built with an SEO-ready structure, and ensured full alignment with the approved design.

Melissa Dainelli — Operations Director, Cognos Global Partners Web Development

FAQ

Frequently Asked Questions About Data & AI Engineering Services

What is the difference between a data engineer and a data scientist?

A data engineer builds and maintains pipelines, storage, and data flows that keep data available and usable. A data scientist works on models, experiments, and analysis based on that data.

As you’d expect, data engineering services come first — without stable pipelines and storage, models don’t move beyond experiments.
How long does it take to build a data pipeline?

Most data pipeline services start delivering usable data flows within 2–4 weeks, depending on scope and number of sources.

TechBar usually sets up simple ingestion flows quickly. More complex pipelines with transformations, validation, and orchestration take longer, especially when we deal with multiple data sources.
Can you help us integrate AI/LLM into our existing systems?

Yes! TechBar supports AI development services by connecting models to your existing tech infrastructure.

Our workflow includes preparing the data layer, building RAG pipelines, and integrating LLM features into the product so they run with real inputs.
How do you ensure data quality in pipelines?
TechBar specialists put the following checks directly into the working process:
Validation rules on ingestion;
- Schema checks and contract enforcement;
- Retry logic and failure management;
- Monitoring and alerts for anomalies.
How much does it cost to hire a data engineer?

Costs depend on experience level, stack, and project scope. You can hire data engineers for a specific task or build a small group around ongoing delivery.

TechBar shares profiles with clear rates upfront so you can compare options before starting.
Can we start with one data specialist and scale the team from there?

Absolutely. Many of our clients have started with one engineer to cover a specific gap, then expanded as the workload grew.

We support this approach with AI engineers for hire and data experts available on demand, allowing you to scale without restarting the process.

Your Data, Engineered to Work

Whether you need a single data engineer or a full AI/ML team, we match you with engineers who have production experience in your stack. Profiles within 48 hours.

Browse Engineer Profiles