Jobs Data & Analytics

Шукаємо у команду Senior DevOps / SRE Engineer (Remote).

Якщо ви маєте бажання працювати в невеликому колективі разом із сильною командою, радо розглянемо ваш відгук! Ми шукаємо не лише сильного DevOps Engineer, а й інженера із досвідом Site Reliability Engineering (SRE), який готовий брати на себе технічне лідерство, ділитися знаннями та допомагати розвиватися іншим членам команди. Націлені на довгострокову співпрацю з компенсацією, що базуватиметься на вашому досвіді роботи.
 

Завантаженість: повна зайнятість — 40 год/тиждень. Робота віддалена. Оплата погодинна.
 

Стек технологій:

- Scripting: Bash | Python | Makefile

- Containers and Orchestration: Kubernetes | Docker | GKE

- Infrastructure as Code (IaC): Terraform | Ansible

- CI/CD & Automation: GitHub Actions | CircleCI | TravisCI

- Cloud Providers & IaaS: AWS | GCP | Azure | Hetzner

- Databases: SQL | NoSQL (PostgreSQL, Redis…)

- Authorization and Security: OpenSSL

- Observability and Monitoring: Prometheus | Grafana | ELK Stack (Elasticsearch, Logstash, Kibana)

- Operating Systems: Linux
 

Site Reliability Engineering (SRE):
- SLI / SLO / SLA | Incident Response | Root Cause Analysis (RCA) | - High Availability | Capacity Planning | Disaster Recovery | 
- Performance Optimization | Reliability Engineering | 
 

Буде плюсом:

- Infrastructure Management: Developing and implementing infrastructure, configuration, and code branching strategies.

- Quality Assurance & Testing: Conducting integration, regression, UI/UX, API, load/stress, and other types of testing.

- Bug Tracking & Issue Management: Identifying, documenting, and tracking software defects and bugs.

- Performance & Optimization: Monitoring and analyzing quality metrics to identify areas for improvement.

- Automated Testing: Experience with automated testing frameworks such as Selenium, Appium, JMeter, etc.

Mentoring & Leadership:
- Досвід менторингу Junior/Middle інженерів, проведення code review, технічних обговорень, knowledge sharing та допомоги команді у професійному розвитку.
 

Вимоги до кандидата:

- Досвід роботи DevOps Engineer та практичний досвід Site Reliability Engineering (SRE)
- Досвід побудови та підтримки високодоступних production-систем із високими вимогами до reliability.

- Досвід роботи з Kubernetes у production-середовищі

- Розуміння принципів високої доступності, масштабування та відмовостійкості систем

- Досвід побудови та підтримки CI/CD процесів

- Англійська мова — Intermediate+

- Уміння слідувати code conventions

- Здатність навчатися

- Здатність наполегливо працювати

- Критичне мислення

- Витривалість

- Спроможність лаконічно та чітко пояснювати свою думку

- Бажання ділитися знаннями, проводити менторинг та допомагати розвиватися іншим членам команди

- Відданість нашим проєктам

- Прозорість
 

Що отримаєте:

- Віддалені умови роботи

- Удосконалення навичок планування та продуктивності

- Робота у вимогливому, але дружньому середовищі серед A-гравців

- Регулярний зворотний зв'язок і допомога в плануванні вашого навчального процесу та кар'єрного зростання

- Незалежність від місцевого ринку та долучення до глобальної економіки

- Підвищення рівня володіння англійською мовою

- Корпоративні навчання, технічні воркшопи та тренінги для постійного підвищення кваліфікації

Our Customer:

A global enterprise organization building modern data, analytics, and AI solutions within a Microsoft-first ecosystem. The company focuses on scalable and secure enterprise platforms that support business intelligence and digital transformation.

Your Tasks:

• Define and drive architecture for enterprise Data, Analytics, and AI solutions across multiple initiatives
• Transform business requirements into scalable, secure, and maintainable technical architectures
• Design solution blueprints, integration approaches, and implementation strategies
• Support delivery teams throughout the full solution lifecycle, from discovery and prototyping to production deployment
• Conduct architecture, design, and code reviews to ensure solution quality and alignment with business goals
• Provide hands-on technical leadership and guidance to engineering teams
• Ensure solutions meet requirements for quality, scalability, security, maintainability, operational readiness, and cost efficiency
• Ensure security, governance, and compliance standards are incorporated into solution designs
• Collaborate with business stakeholders, architects, engineering teams, and governance functions to drive successful delivery
• Participate in troubleshooting, risk assessment, estimation, and technical decision-making activities

Required Experience and Skills:

• 15+ years of experience in IT, including 10+ years in Solution Architecture roles focused on enterprise Data, Analytics, and AI platforms
• 10+ years of hands-on experience with Azure-based data solutions, including Azure Data Factory, Databricks, Azure Data Lake, SQL, dimensional modeling, ETL/ELT processes, and data orchestration
• Strong expertise in Power BI architecture, including semantic models, DAX, Power Query, governance, workspace management, performance optimization, and enterprise reporting solutions
• Proven experience delivering end-to-end architecture solutions, including solution design, prototyping, implementation support, technical reviews, and production readiness activities
• Strong knowledge of enterprise security, governance, compliance, and architecture best practices
• Ability to translate complex business challenges into practical and delivery-focused technical solutions
• Experience providing technical leadership in distributed enterprise environments
• Experience working with distributed teams and external partners in enterprise delivery environments
• Strong troubleshooting, analytical, estimation, decision-making, and risk management skills
• Ability to work effectively with enterprise architects, business stakeholders, and cross-functional teams
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field
• Excellent communication skills in English

Would be a Plus:

• Experience with enterprise AI solutions and Microsoft AI services, including Azure OpenAI, Azure AI Search, AI Foundry, Copilot Studio, Semantic Kernel, and Prompt Flow
• Experience with Microsoft Fabric and Power Platform governance practices
• Experience with Azure DevOps, GitHub Enterprise, and CI/CD implementation for Data, Analytics, or AI platforms
• Knowledge of Microsoft security and governance services such as Entra, Purview, and Key Vault
• Experience working in regulated industries, including life sciences, healthcare, or pharmaceuticals
• Microsoft certifications related to Azure, Data, Analytics, Power BI, or AI Architecture
• Experience with Python, data observability, and monitoring solutions

Working conditions

5-day working week, 

8-hour working day;

Remote work.

Project Description:

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

Responsibilities:

- Design, build, and maintain ETL/ELT pipelines on AWS
- Migrate existing data pipelines and workloads from legacy/on-prem systems to AWS
- Develop and optimize data models for data warehouse/data lake (Redshift, S3)
- Build and orchestrate data workflows (Glue, Step Functions, Lambda, EMR)
- Implement data quality checks, validation, and reconciliation processes
- Optimize pipeline performance and manage compute/storage costs
- Ensure data security and access control (IAM, KMS, encryption)
- Monitor and troubleshoot pipeline failures (CloudWatch, logging)
- Collaborate with data analysts, BI developers, and architects on data requirements
- Document data pipelines, architecture, and operational runbooks

Mandatory Skills Description:

- 5+ years of experience
- Hands-on experience building data pipelines on AWS (Glue, EMR, Lambda, Step Functions)
- Strong SQL and experience with data warehousing (Redshift, dimensional modeling)
- Proficiency in Python or Scala for data engineering
- Experience with S3-based data lake architecture (partitioning, cataloging, formats like Parquet)
- Experience migrating data pipelines from on-prem or other cloud platforms
- Understanding of data governance, security, and access control on AWS
- Experience with CI/CD for data pipelines

Nice-to-Have Skills Description:

- AWS certification (Data Analytics Specialty or Data Engineer Associate)
- Experience with streaming data (Kinesis, MSK/Kafka)
- Familiarity with Infrastructure as Code (Terraform/CloudFormation)
- Experience with orchestration tools (Airflow, dbt)
- Experience in financial domain

Languages:

English: C2 Proficient

Project Description:

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

Responsibilities:

- Design, build, and maintain ETL/ELT pipelines on AWS
- Migrate existing data pipelines and workloads from legacy/on-prem systems to AWS
- Develop and optimize data models for data warehouse/data lake (Redshift, S3)
- Build and orchestrate data workflows (Glue, Step Functions, Lambda, EMR)
- Implement data quality checks, validation, and reconciliation processes
- Optimize pipeline performance and manage compute/storage costs
- Ensure data security and access control (IAM, KMS, encryption)
- Monitor and troubleshoot pipeline failures (CloudWatch, logging)
- Collaborate with data analysts, BI developers, and architects on data requirements
- Document data pipelines, architecture, and operational runbooks

Mandatory Skills Description:

- 5+ years of experience
- Hands-on experience building data pipelines on AWS (Glue, EMR, Lambda, Step Functions)
- Strong SQL and experience with data warehousing (Redshift, dimensional modeling)
- Proficiency in Python or Scala for data engineering
- Experience with S3-based data lake architecture (partitioning, cataloging, formats like Parquet)
- Experience migrating data pipelines from on-prem or other cloud platforms
- Understanding of data governance, security, and access control on AWS
- Experience with CI/CD for data pipelines

Nice-to-Have Skills Description:

- AWS certification (Data Analytics Specialty or Data Engineer Associate)
- Experience with streaming data (Kinesis, MSK/Kafka)
- Familiarity with Infrastructure as Code (Terraform/CloudFormation)
- Experience with orchestration tools (Airflow, dbt)
- Experience in financial domain

• This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
  During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
  The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
  This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

   

• Responsibilities:

  - Agentic AI Migration Framework development — design and build an agentic AI framework to automate and accelerate migration workflowsж
  - Pattern library build — develop and maintain a library of reusable migration patterns/templates for common conversion scenarios;
  -Automated conversion pipeline — build pipelines that automatically convert/transform code and configurations (e.g., Matillion jobs) as part of the migration process;
  -GitHub Copilot integration for code generation — integrate GitHub Copilot into the migration workflow to accelerate code generation and conversion tasks;
  -Support factory pods — provide technical support, tooling, and troubleshooting to migration factory pods executing migrations at scale

   

• Mandatory Skills Description:

  - 2 month project with posibility of extentention

  - Python — strong hands-on Python development experience for building tools and automation
  - GenAI/LLM integration — experience integrating GenAI/LLM capabilities into applications, workflows, or tooling
  - Prompt engineering — hands-on experience designing, testing, and optimizing prompts for LLM-based tools
  - AWS Bedrock — hands-on experience with AWS Bedrock for building GenAI-powered applications
  - GitHub Copilot — practical experience using and/or integrating GitHub Copilot into development workflows
  - Automation frameworks — experience designing and building automation frameworks/tooling from scratch
  - Testing — strong experience with test automation and validation frameworks to ensure conversion accuracy
  - Matillion JSON parsing — experience parsing, interpreting, and transforming Matillion job definitions (JSON format)

   

• Nice-to-Have Skills Description:

  Experience in financial domain

   

• Languages:
  • English: C2 Proficient

Project Description:

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

Responsibilities:

- Agentic AI Migration Framework development — design and build an agentic AI framework to automate and accelerate migration workflowsж
- Pattern library build — develop and maintain a library of reusable migration patterns/templates for common conversion scenarios;
-Automated conversion pipeline — build pipelines that automatically convert/transform code and configurations (e.g., Matillion jobs) as part of the migration process;
-GitHub Copilot integration for code generation — integrate GitHub Copilot into the migration workflow to accelerate code generation and conversion tasks;
-Support factory pods — provide technical support, tooling, and troubleshooting to migration factory pods executing migrations at scale

Mandatory Skills Description:

- Python — strong hands-on Python development experience for building tools and automation
- GenAI/LLM integration — experience integrating GenAI/LLM capabilities into applications, workflows, or tooling
- Prompt engineering — hands-on experience designing, testing, and optimizing prompts for LLM-based tools
- AWS Bedrock — hands-on experience with AWS Bedrock for building GenAI-powered applications
- GitHub Copilot — practical experience using and/or integrating GitHub Copilot into development workflows
- Automation frameworks — experience designing and building automation frameworks/tooling from scratch
- Testing — strong experience with test automation and validation frameworks to ensure conversion accuracy
- Matillion JSON parsing — experience parsing, interpreting, and transforming Matillion job definitions (JSON format)

Nice-to-Have Skills Description:

Experience in financial domain

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

• Responsibilities:

  - Design, build, and maintain ETL/ELT pipelines on AWS
  - Migrate existing data pipelines and workloads from legacy/on-prem systems to AWS
  - Develop and optimize data models for data warehouse/data lake (Redshift, S3)
  - Build and orchestrate data workflows (Glue, Step Functions, Lambda, EMR)
  - Implement data quality checks, validation, and reconciliation processes
  - Optimize pipeline performance and manage compute/storage costs
  - Ensure data security and access control (IAM, KMS, encryption)
  - Monitor and troubleshoot pipeline failures (CloudWatch, logging)
  - Collaborate with data analysts, BI developers, and architects on data requirements
  - Document data pipelines, architecture, and operational runbooks

   

• Mandatory Skills Description:

  - 5+ years of experience

  - 2 month project with posibility of extentention
  - Hands-on experience building data pipelines on AWS (Glue, EMR, Lambda, Step Functions)
  - Strong SQL and experience with data warehousing (Redshift, dimensional modeling)
  - Proficiency in Python or Scala for data engineering
  - Experience with S3-based data lake architecture (partitioning, cataloging, formats like Parquet)
  - Experience migrating data pipelines from on-prem or other cloud platforms
  - Understanding of data governance, security, and access control on AWS
  - Experience with CI/CD for data pipelines

   

• Nice-to-Have Skills Description:

  - AWS certification (Data Analytics Specialty or Data Engineer Associate)
  - Experience with streaming data (Kinesis, MSK/Kafka)
  - Familiarity with Infrastructure as Code (Terraform/CloudFormation)
  - Experience with orchestration tools (Airflow, dbt)
  - Experience in financial domain

• Languages:
  • English: C2 Proficient

Project Description:

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.

Responsibilities:

-Target-state architecture design — define and own the target-state AWS architecture across compute, data, and orchestration layers
-HLD/LLD ownership — produce and own High-Level Design and Low-Level Design documentation for migration workloads
-ARB submissions — prepare and present architecture proposals to the Architecture Review Board for approval
-Technical decisions — make and document key technical decisions across the migration program, balancing trade-offs (cost, performance, scalability)
Pattern definition — define reusable architecture and migration patterns for use across teams/pods
-Agentic AI framework architecture — design the architecture for the Agentic AI migration framework, including its integration with the broader technical landscape

Mandatory Skills:

AWS Database Migration Service

Mandatory Skills Description:

- 5+ years experience
- hands-on experince with AWS Migration
- AWS Glue, Step Functions, Lambda, EventBridge — deep hands-on experience designing solutions using these core AWS services
- IaC (Terraform) — strong experience defining and managing infrastructure as code with Terraform
- Data platform architecture — proven experience architecting data platforms (data lakes, warehouses, pipelines) at scale
- PySpark — hands-on experience with PySpark for large-scale data processing
- CI/CD — experience designing and implementing CI/CD pipelines for infrastructure and data workflows
- AI/ML frameworks — working knowledge of AI/ML frameworks and their application within architecture design
- GitHub Copilot — practical experience using GitHub Copilot within development/architecture workflows

Nice-to-Have Skills Description:

- AWS certification (Data Analytics Specialty or Solutions Architect)
- Experience with BI tools (QuickSight, Tableau, Power BI)
- Infrastructure as Code experience (Terraform/CloudFormation)
- Industry experience relevant to your business
- Exposure to streaming data (Kinesis) or ML pipelines (SageMaker)
- experince in financial domain

Languages:

English: C2 Proficient

Project description

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.

Responsibilities

-Target-state architecture design — define and own the target-state AWS architecture across compute, data, and orchestration layers

-HLD/LLD ownership — produce and own High-Level Design and Low-Level Design documentation for migration workloads

-ARB submissions — prepare and present architecture proposals to the Architecture Review Board for approval

-Technical decisions — make and document key technical decisions across the migration program, balancing trade-offs (cost, performance, scalability)

Pattern definition — define reusable architecture and migration patterns for use across teams/pods

-Agentic AI framework architecture — design the architecture for the Agentic AI migration framework, including its integration with the broader technical landscape

Skills

Must have

5+ years experience

hands-on experince with AWS Migration

AWS Glue, Step Functions, Lambda, EventBridge — deep hands-on experience designing solutions using these core AWS services

IaC (Terraform) — strong experience defining and managing infrastructure as code with Terraform

Data platform architecture — proven experience architecting data platforms (data lakes, warehouses, pipelines) at scale

PySpark — hands-on experience with PySpark for large-scale data processing

CI/CD — experience designing and implementing CI/CD pipelines for infrastructure and data workflows

AI/ML frameworks — working knowledge of AI/ML frameworks and their application within architecture design

GitHub Copilot — practical experience using GitHub Copilot within development/architecture workflows

Nice to have

AWS certification (Data Analytics Specialty or Solutions Architect)

Experience with BI tools (QuickSight, Tableau, Power BI)

Infrastructure as Code experience (Terraform/CloudFormation)

Industry experience relevant to your business

Exposure to streaming data (Kinesis) or ML pipelines (SageMaker)

experince in financial domain

Other

Languages

English: C2 Proficient

• This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
  During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
  The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
  This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

• Responsibilities:

  - Design, build, and maintain ETL/ELT pipelines on AWS
  - Migrate existing data pipelines and workloads from legacy/on-prem systems to AWS
  - Develop and optimize data models for data warehouse/data lake (Redshift, S3)
  - Build and orchestrate data workflows (Glue, Step Functions, Lambda, EMR)
  - Implement data quality checks, validation, and reconciliation processes
  - Optimize pipeline performance and manage compute/storage costs
  - Ensure data security and access control (IAM, KMS, encryption)
  - Monitor and troubleshoot pipeline failures (CloudWatch, logging)
  - Collaborate with data analysts, BI developers, and architects on data requirements
  - Document data pipelines, architecture, and operational runbooks

• Mandatory Skills Description:

  - 5+ years of experience
  - Hands-on experience building data pipelines on AWS (Glue, EMR, Lambda, Step Functions)
  - Strong SQL and experience with data warehousing (Redshift, dimensional modeling)
  - Proficiency in Python or Scala for data engineering
  - Experience with S3-based data lake architecture (partitioning, cataloging, formats like Parquet)
  - Experience migrating data pipelines from on-prem or other cloud platforms
  - Understanding of data governance, security, and access control on AWS
  - Experience with CI/CD for data pipelines

• Nice-to-Have Skills Description:

  - AWS certification (Data Analytics Specialty or Data Engineer Associate)
  - Experience with streaming data (Kinesis, MSK/Kafka)
  - Familiarity with Infrastructure as Code (Terraform/CloudFormation)
  - Experience with orchestration tools (Airflow, dbt)
  - Experience in financial domain

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with client's engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate client's rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.
This is the kind of engagement where your recommendations directly shape a multi-phase, multi-million-pound programme: the target-state framework you produce here becomes the blueprint that a larger delivery team will execute against. Perfect opportunity for combining deep data engineering knowledge with architecture leadership, stakeholder influence, and structured delivery planning inside a Tier 1 financial services environment

• Responsibilities:

  - Agentic AI Migration Framework development — design and build an agentic AI framework to automate and accelerate migration workflowsж
  - Pattern library build — develop and maintain a library of reusable migration patterns/templates for common conversion scenarios;
  -Automated conversion pipeline — build pipelines that automatically convert/transform code and configurations (e.g., Matillion jobs) as part of the migration process;
  -GitHub Copilot integration for code generation — integrate GitHub Copilot into the migration workflow to accelerate code generation and conversion tasks;
  -Support factory pods — provide technical support, tooling, and troubleshooting to migration factory pods executing migrations at scale

• Mandatory Skills Description:

  - Python — strong hands-on Python development experience for building tools and automation
  - GenAI/LLM integration — experience integrating GenAI/LLM capabilities into applications, workflows, or tooling
  - Prompt engineering — hands-on experience designing, testing, and optimizing prompts for LLM-based tools
  - AWS Bedrock — hands-on experience with AWS Bedrock for building GenAI-powered applications
  - GitHub Copilot — practical experience using and/or integrating GitHub Copilot into development workflows
  - Automation frameworks — experience designing and building automation frameworks/tooling from scratch
  - Testing — strong experience with test automation and validation frameworks to ensure conversion accuracy
  - Matillion JSON parsing — experience parsing, interpreting, and transforming Matillion job definitions (JSON format)

Cloudfresh ⛅️ is a Global Google Cloud Premier Partner, Zendesk Premier Partner, Asana Solutions Partner, GitLab Select Partner, Hubspot Platinum Partner, Okta Activate Partner, and Microsoft Partner.

Since 2017, we’ve been specializing in the implementation, migration, integration, audit, administration, support, and training for top-tier cloud solutions. Our products focus on cutting-edge cloud computing, advanced location and mapping, seamless collaboration from anywhere, unparalleled customer service, and innovative DevSecOps.

We're looking for an AI Architect to become our lead technical expert for client AI solutions and to help stand up our AI Competence Centre and internal AI Lab.

This is a client-facing technical role first, with a build-and-lead dimension. Expect roughly 60% solution architecture and pre-sales engineering for enterprise clients, and 40% establishing the AI Lab and mentoring engineers. You'll technically lead and raise the bar for the team - this is a technical leadership role, not a people-management line.
Reports to: Head of AI.

Requirements

• Experience & seniority. 5+ years in software, data, or ML engineering, including 3+ years delivering ML/AI in production and 2+ years in a client-facing architect or technical lead capacity.
• Cloud & AI infrastructure. Production-level experience on Google Cloud Platform, especially the Vertex AI suite, plus integrating third-party model providers such as Anthropic.
• GenAI engineering. Strong hands-on knowledge of RAG, agentic workflows, orchestration frameworks (LangChain, LlamaIndex), vector databases, and prompt/context engineering.
• Production & MLOps. Hands-on with the ML lifecycle, CI/CD for models, automated deployment, and cloud cost management - including right-sizing models (lighter vs. heavier) to control spend.
• Evaluation, guardrails & observability. Practical experience building LLM evaluations, guardrails, and monitoring for accuracy, safety, latency, and cost in production.
• AI governance & security. Familiarity with data governance, model risk, and EU regulation (EU AI Act, GDPR) as it applies to enterprise AI — we're primarily EU-based.
• Pre-sales & client experience. Proven experience presenting to enterprise clients, scoping technical projects, and translating complex AI concepts into clear business value.
• Technical leadership. Experience guiding engineers, reviewing architecture and code, and raising team capability.
• Languages. Native Ukrainian — required, as you'll run Ukrainian-language pre-sales — and strong English (B2+/C1 minimum).

• Mindset & agility. An intrapreneurial drive to build an AI Lab from scratch inside an established business, plus the agility to move smoothly between client pre-sales and deep technical architecture.

   

Responsibilities

• Solution architecture. Design and own end-to-end AI systems for strategic enterprise clients - reference architectures, data and retrieval pipelines, agentic layers, and integration with existing enterprise systems - with a clear path from prototype to scalable, secure, observable production.
• Pre-sales & consulting. Partner with sales to qualify and win AI opportunities: run technical discovery, scope solutions, size effort and cost, and translate AI into business value, ROI, and risk for C-level stakeholders.
• Technical leadership & mentorship. Set architecture and engineering standards, run design and code reviews, and level up engineers across the AI practice.
• AI Lab & R&D. Stand up and run the internal AI Lab - rapid client prototypes and cost-effective internal AI tooling - with a clear bar for what graduates into production.
• Partner collaboration. Work with our core AI partners (Google Cloud / Vertex AI, Anthropic) to build joint solutions, access early capabilities, and shape our technical roadmap.
• Governance, security & cost. Build guardrails, evaluation, and observability into every solution; keep architectures compliant (EU AI Act, GDPR) and cost-aware through right-sized model selection.

Would be a plus

• Google Cloud Professional Machine Learning Engineer and/or Professional Cloud Architect certification.
• Hands-on enablement or certification with Anthropic and Vertex AI generative-AI stacks.
• Direct experience mapping solutions to the EU AI Act risk framework.
• Fine-tuning, distillation, or model-optimization experience.
• Production experience with voice/multimodal AI (e.g., ElevenLabs) or AI-assisted engineering tooling (e.g., Cursor).
• Contributions to the open-source GenAI ecosystem.

Work conditions

• Flexible Work Format: Work remotely, allowing you to balance your professional and personal life efficiently.
• Training with Leading Cloud Products: Access in-depth training on cutting-edge cloud and AI solutions, enhancing your expertise and equipping you with the tools to succeed in an ever-evolving industry.
• International Collaboration: Work alongside A-players and seasoned professionals in the cloud industry.
• Global Exposure: Expand your expertise by engaging with international markets across the EMEA and CEE regions.
• Vibrant Team Environment: Be part of an innovative, dynamic team that fosters both personal and professional growth, creating opportunities for you to advance in your career.
  
  When applying to this position, you consent to the processing of your personal data by CLOUDFRESH for the purposes necessary to conduct the recruitment process, in accordance with Regulation (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016 (GDPR).
  Additionally, you agree that CLOUDFRESH may process your personal data for future recruitment processes. You can withdraw this consent at any time.

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.
During this Mobilisation Phase, you'll work jointly with engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.
The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.

• Responsibilities:

  -Target-state architecture design — define and own the target-state AWS architecture across compute, data, and orchestration layers
  -HLD/LLD ownership — produce and own High-Level Design and Low-Level Design documentation for migration workloads
  -ARB submissions — prepare and present architecture proposals to the Architecture Review Board for approval
  -Technical decisions — make and document key technical decisions across the migration program, balancing trade-offs (cost, performance, scalability)
  Pattern definition — define reusable architecture and migration patterns for use across teams/pods
  -Agentic AI framework architecture — design the architecture for the Agentic AI migration framework, including its integration with the broader technical landscape

• Mandatory Skills Description:

  - 5+ years experience
  - hands-on experince with AWS Migration
  - AWS Glue, Step Functions, Lambda, EventBridge — deep hands-on experience designing solutions using these core AWS services
  - IaC (Terraform) — strong experience defining and managing infrastructure as code with Terraform
  - Data platform architecture — proven experience architecting data platforms (data lakes, warehouses, pipelines) at scale
  - PySpark — hands-on experience with PySpark for large-scale data processing
  - CI/CD — experience designing and implementing CI/CD pipelines for infrastructure and data workflows
  - AI/ML frameworks — working knowledge of AI/ML frameworks and their application within architecture design
  - GitHub Copilot — practical experience using GitHub Copilot within development/architecture workflows

• Nice-to-Have Skills Description:

  - AWS certification (Data Analytics Specialty or Solutions Architect)
  - Experience with BI tools (QuickSight, Tableau, Power BI)
  - Infrastructure as Code experience (Terraform/CloudFormation)
  - Industry experience relevant to your business
  - Exposure to streaming data (Kinesis) or ML pipelines (SageMaker)
  - experince in financial domain

Key details:
Start date: 1 October 2026
Duration: initially until 31 December 2026
Option to extend: until 31 December 2027
Workload: approx. 400 hours in 2026, with an option to extend to approx. 1,680 hours in 2027
Location: predominantly remote (approx. 80%), with occasional assignments in Hanover or at the client’s premises.

Project background: As part of an AI and machine learning initiative, we are driving forward the further development and support of data-driven solutions for various specialist areas within the social security sector. We are seeking support in the development, optimisation and operation of modern data and AI platforms.

Responsibilities:

• Carrying out data collection, data cleaning and data transformation
• Creating and maintaining dashboards for data visualisation
• Setting up and maintaining data pipelines
• Identifying and testing new areas of application for AI-based solutions

Requirements:

• At least 2 years’ experience with SQL, data transformations, data pipelines and data storage (DB/DWH)
• At least 2 years’ practical experience in software development using Python
• At least 2 years’ experience in developing ETL pipelines
• At least 2 years’ experience in data-driven projects (please specify the project objective of each reference project, your role and the tasks you carried out)
• At least 2 years’ experience in agile development teams
• German language skills at C1 level or higher !!!

Desirable:

• Two years’ professional experience in CI/CD development
• Two years’ professional experience with Jenkins and ArgoCD
• Two years’ professional experience with Argo Workflows
• Two years’ professional experience with Kubeflow or OpenShift AI in a machine learning environment
• Two years’ professional experience with machine learning and the practical application of ML models
• Two years’ professional experience with unit and integration testing
• Two years’ experience in the field of statutory social insurance
• Two years’ project work in a SAFe environment

Job Description
Extensive software development experience, including experience working on complex backend or data infrastructure projects.
Strong hands-on experience with Scala.

Experience with Apache Iceberg or similar table formats and data lake technologies.

Hands-on experience developing backend services, data-processing engines, infrastructure components.
Programming experience in C /or C++ is highly desirable / preferred, the role is primarily focused on backend/system-level engineering rather than full-stack application development.
Deep understanding of distributed systems, data processing, storage engines, or data infrastructure.

Strong knowledge of data consistency, transactions, schema evolution, partitioning, compaction, and performance optimization.

Proven ability to troubleshoot complex production and customer issues.

Strong ownership mindset and the ability to operate effectively in areas with limited documentation or historical knowledge.

Excellent communication and collaboration skills.

Preferred Qualifications: 
Big Data Frameworks: Hands-on experience with distributed data-processing technologies such as Apache Spark, Flink, Trino, or Presto.
Cloud Object Storage: Experience working with cloud storage platforms, including Amazon S3, Google Cloud Storage (GCS), or Azure Data Lake Storage (ADLS).
Legacy Modernization: Proven track record of modernizing or gradually improving a mature codebase without disrupting existing customers.
Job Responsibilities
Architect & Build: Lead the architecture, design, and implementation of highly scalable, complex backend systems and robust data infrastructure using Scala.
Data Lake Engineering: Design and manage enterprise-grade data lake solutions leveraging Apache Iceberg or equivalent table formats, ensuring optimal data consistency, transactions, schema evolution, partitioning, and compaction.
Performance Optimization: Maximize system throughput, minimize latency, and optimize resource utilization across large-scale distributed data processing systems.
Production Excellence: Systematically diagnose, troubleshoot, and resolve complex, high-impact production and customer-facing infrastructure issues.
Codebase Modernization: Champion the gradual improvement and modernization of a mature, production-critical codebase without causing disruption to active customers or services.
Technical Leadership & Collaboration: Act as a technical authority, fostering strong communication and collaboration across engineering teams to align on technical objectives.
Navigate Ambiguity: Exercise a strong ownership mindset to operate effectively and deliver high-quality outcomes in areas with limited documentation or historical knowledge.
Department/Project Description
We are seeking an expert Scala Engineer to design, optimize, and scale our next-generation multi-cloud data lake house architecture across AWS and Azure. The ideal candidate is a backend/system engineer with strong experience building engines, infrastructure, or high-performance backend components. In this role, you will leverage Qlik Open Lakehouse (integrated with Qlik Talend Cloud) to establish a low-overhead, high-throughput data foundation. You will manage automated data ingestion, implement continuous table optimization, handle cross-cloud data mirroring, and ensure seamless multi-engine access without vendor lock-in.