DataArt

Project overview

This project focuses on building and enhancing an enterprise data warehouse platform that supports data driven decision making across complex business domains. The work involves designing scalable data models, ingestion pipelines, and transformation logic to ensure high quality and consistent data.

Team

You will be part of a collaborative data team working alongside data designers, data engineers, and platform specialists. The team operates in an agile environment, shares knowledge actively, and follows established data design standards and review practices.

Position overview

We are looking for a Senior Data Designer who can operate as the bridge between business stakeholders and engineering — someone equally comfortable running a requirements workshop and designing a Kimball-style dimensional model. This is not a pure modelling role: you'll investigate source systems, challenge and shape business requirements before any design work starts, and turn that understanding into governed, well-documented data platform designs that engineering teams can implement. You will contribute to data ingestion design, transformation logic, and data harmonization while working closely with stakeholders and platform teams.

Technology stack

Core: SQL, dimensional/Kimball data modelling, enterprise data warehouse tools, cloud data platforms
Additional technologies used within the project: Python, PySpark, data profiling tooling

Responsibilities

• Design and document Enterprise Data Warehouse objects: data models, source-to-target mappings, model diagrams, and supporting design documentation that engineers deliver from
• Work with stakeholders and source-system SMEs to onboard new data sources and adapt designs to what each source provides
• Clarify the intent behind requirements before designing: why the stakeholder needs it and whether existing structures already cover it
• Profile and analyze source data to validate design assumptions
• Define data quality rules as part of designs for downstream teams to implement
• Investigate reported data issues, identify root causes, and propose design-level fixes
• Take designs through peer and design review; give and take constructive challenge
• Keep stakeholders informed: raise issues early, ask instead of assuming, follow up until unblocked

Requirements

• Strong practical foundations of dimensional modeling — able to design warehouse models from raw source data and explain the reasoning behind design choices
• Hands-on experience producing source-to-target mapping documentation
• Working understanding of data platform layering and data load approaches
• Confident SQL for data profiling and validation
• Strong written and spoken English; comfortable gathering requirements and managing stakeholder expectations
• Attention to detail; comfortable working in a governed environment with reviews and design standards, and data privacy/PII classification requirements

Nice to have

• SAP as a source system
• Retail domain experience
• Agile delivery experience

Client

Our client is an international SaaS company developing tools that empower businesses to create highly engaging digital experiences. Their solutions are trusted by thousands of organizations worldwide to collect, analyze, and act on user data effectively.

Project overview

The project is an interactive form builder designed for marketers, researchers, HR, sales, and customer experience teams to create engaging, conversational online forms, surveys, and quizzes.

Key features:
Visually appealing conversational design optimized for high completion rates
AI-powered data interpretation
Workflow and logic builders for personalized experiences
Brand customization options
Seamless integrations with various tools to automate data collection and streamline workflows

Position overview

We are seeking a Full Stack Engineer who builds with AI in the loop — both as a daily development practice and as a product capability. You are fluent with coding agents (Claude Code, Cursor, Copilot) and ship faster because of them, not despite them. You understand how to design, prompt, evaluate, and operate LLM-backed features in production — including model selection, context engineering, evals, guardrails, and cost and latency tradeoffs.

Beyond AI, you have deep Golang and React/TypeScript expertise and own features end-to-end: from understanding business requirements, through writing and testing code, to deployment and production monitoring. This role requires strong autonomy, proactive communication, and the ability to deliver reliable, scalable solutions in a modern cloud environment.

This position operates according to the CET time zone.

Responsibilities

• Design and ship LLM-powered product features: prompt design, retrieval, tool and function calling, structured outputs, streaming, evaluation harnesses, and guardrails.
• Operate AI features in production: monitor quality, latency, cost, and failure modes; iterate on prompts, models, and architectures based on real usage.
• Use AI coding tools (Claude Code, Cursor, Copilot) as a core part of your workflow to accelerate delivery without compromising code quality.
• Design, develop, and maintain backend services using Golang and frontend applications using React/TypeScript.
• Collaborate with product managers, designers, and other engineers to translate business requirements into technical solutions.
• Implement event-driven architectures leveraging Kafka for real-time data processing.
• Manage and optimize AWS environments, ensuring scalability and reliability.
• Define and maintain infrastructure using Terraform and deploy services in Kubernetes clusters.
• Build and maintain CI/CD pipelines in Jenkins for automated deployments.
• Ensure high-quality code through comprehensive testing and code reviews.
• Monitor application performance and troubleshoot issues in production environments.
• Contribute to UI/UX improvements and ensure seamless integration between frontend and backend.

Requirements

• Demonstrable production experience shipping LLM-powered features. Be ready to walk through a feature you built: prompts, evaluation approach, failure modes you fixed, and how you measured success.
• Hands-on with at least one major LLM provider's API (Anthropic, OpenAI, AWS Bedrock, or equivalent), including streaming, tool use, and structured outputs.
• Daily use of AI coding assistants in your workflow; able to articulate where they help, where they hurt, and how you maintain code quality.
• Strong, in-depth knowledge of Golang.
• Experience delivering complex backend systems from design to production.
• Solid background with Kafka and event-driven architectures.
• Hands-on experience managing AWS environments.
• Proficiency with Terraform for infrastructure-as-code.
• Experience deploying and operating services in Kubernetes.
• Building CI/CD pipelines in Jenkins.
• Proven ability to ensure quality through comprehensive testing.
• Strong proficiency in React and TypeScript.
• Experience contributing to front-end features, UI components, and client-side logic.
• Ability to seamlessly integrate frontend with backend services.
• Excellent English communication skills, written and spoken.
• Self-driven and able to work independently while managing workload effectively.

Nice to have

• Experience with agent frameworks, multi-step tool-use loops, or RAG systems at scale.
• Built or maintained an evaluation pipeline (offline evals, LLM-as-judge, human review loops).
• Open-source contributions, published writing, or talks on applied AI engineering.

Project overview

The project focuses on building a modern observability platform for AI driven applications and autonomous agent ecosystems. The solution provides comprehensive tracing, metrics collection, logging, and monitoring capabilities across cloud based services, enabling reliable operations, performance analysis, and operational insights.

Position overview

We are looking for a Senior Observability Engineer with strong experience in OpenTelemetry, AWS observability services, and Python based instrumentation. You will design and implement observability solutions for AI powered applications and agent based systems, ensuring end to end visibility, monitoring, tracing, and telemetry collection across distributed environments.

Team

You will work with observability engineers, software engineers, AI engineers, cloud specialists, and platform architects. The team collaborates closely on designing scalable telemetry solutions, defining observability standards, and supporting production grade AI and cloud workloads.

Technology stack

Python, OpenTelemetry, AWS Distro for OpenTelemetry (ADOT), AWS CloudWatch, AWS X Ray, AWS AgentCore Observability, LangChain, W3C Trace Context, W3C Baggage Propagation, YAML, Git, CI/CD, Cloud Infrastructure
 

Responsibilities

• Develop and maintain observability solutions for cloud native and AI powered applications
• Implement OpenTelemetry instrumentation for Python services and distributed systems
• Design telemetry pipelines using AWS Distro for OpenTelemetry
• Configure and optimize metrics, logs, and tracing integrations with AWS CloudWatch and AWS X Ray
• Implement distributed tracing using W3C Trace Context and Baggage propagation standards
• Integrate and monitor LangChain based workflows and AI application components
• Design structured logging standards and telemetry data models
• Collaborate with engineering teams to define observability requirements and establish monitoring best practices
• Support performance analysis, troubleshooting, and incident investigation activities
• Contribute to the evolution of observability architecture for agent based systems and AI workloads

Requirements

• 3+ years of experience in observability engineering
• Experience implementing OpenTelemetry instrumentation for Python applications and services
• Experience with AWS CloudWatch including Metrics, Logs, and Structured Logs
• Experience integrating AWS X Ray for distributed tracing and performance analysis
• Strong understanding of telemetry concepts including metrics, traces, logs, and distributed observability
• Experience working with cloud environments and production systems
• Knowledge of W3C Trace Context and Baggage propagation standards
• Experience working with source control systems and collaborative development practices

Nice to have

• Experience with LangGraph or Strands agent frameworks
• Knowledge of OpenTelemetry GenAI SIG semantic conventions
• Experience configuring AWS Distro for OpenTelemetry collectors and export pipelines
• Familiarity with AWS AgentCore Observability capabilities
• Experience supporting observability for AI applications, agentic systems, or LLM powered solutions
• Knowledge of cloud native monitoring and distributed systems architectures

Client

Our client is the UK’s leading development finance institution, dedicated to supporting companies in developing countries and driving sustainable growth.

Position overview

We are looking for a Senior/Lead Data Engineer to join a distributed team working on modern cloud-based data solutions within the Azure ecosystem. The role involves designing, developing, and maintaining scalable data pipelines and integrations that support business-critical reporting, analytics, and operational processes.

You will work closely with cross-functional teams, contributing to architecture decisions, delivery processes, and continuous platform improvements while ensuring high standards of quality and reliability.

Responsibilities

• Design, develop, and maintain data pipelines and ETL/ELT processes using Azure technologies
• Build and support scalable solutions using Azure Data Factory (ADF), Azure SQL, and other Azure services
• Develop backend logic and automation using Python, Azure Functions, and Logic Apps
• Collaborate with stakeholders, analysts, and engineering teams to gather requirements and deliver data solutions
• Monitor, troubleshoot, and optimize data workflows and integrations
• Participate in code reviews, technical discussions, and solution design activities
• Contribute to CI/CD and deployment processes within the Azure environment
• Ensure data quality, security, and performance best practices are followed
   

Requirements

• Strong hands-on experience with Azure Data Factory (ADF)
• Solid experience with Azure SQL and the Azure Data Stack
• Strong Python development skills
• Experience working with Azure Logic Apps and Azure Function Apps
• Good understanding of cloud-based data architecture and integration patterns
• Experience building and maintaining scalable data pipelines
• Familiarity with Azure DevOps, Git, and CI/CD pipelines
• Experience working in Agile development environments
• Strong communication and collaboration skills
• Upper-Intermediate or higher English level

Nice to have

• Experience leading technical initiatives or mentoring engineers
• Knowledge of data governance and security best practices
• Experience with infrastructure automation or monitoring tools
• Exposure to financial services or development finance domains