THE ROLE

We’re building a new capability inside Platform Engineering, within our Infrastructure Shared Services (ISS) organisation: Data Platform Engineering. Our goal is to create a platform that removes friction, providing a consistent way to build, operate, and evolve data-driven services with clear ownership, guardrails, and a strong developer experience. Everpure’s own platform thinking is anchored in the idea of a unified data plane - a single fabric that connects data across locations and delivers consistent, secure data services across protocols and applications.

In this role, you’ll build and operate a trusted, scalable, and intelligent data platform that empowers teams to make faster data-driven decisions while shaping a culture of ownership and innovation to meet the needs of an ever-changing business and technology landscape.

WHAT YOU'LL DO

• Design, build, and operate the core data platform services that enable teams to ingest, model, query, and publish data seamlessly.
• Architect a multi-engine environment (e.g., Trino/Starburst, Dremio, ClickHouse, Postgres/pgLake) with an Iceberg-based lakehouse, prioritizing interoperability, operability, and security.
• Drive platform evolution through experimentation, measuring performance, reliability, and cost trade-offs to standardize high-impact solutions.
• Establish durable corporate knowledge by developing strong defaults, comprehensive documentation, and intuitive self-service interfaces.
• Manage core lakehouse components, including table layout conventions, catalogue integration, and critical lifecycle maintenance patterns such as compaction and optimization.
• Implement robust ingestion patterns for both batch and streaming data, ensuring clear SLAs and highly observable failure modes.
• Enable multi-engine access by configuring connectors and catalog permissions while maintaining strict guardrails for a consistent user experience.

WHAT YOU BRING

• 6+ years of experience in the design and development of data pipeline automation, specifically extracting data from API-based sources.
• Technical expertise in developing complex automation frameworks, data modeling, and ETL processes using SQL, Python, DBT, Apache Airflow, or similar languages.
• Full-stack proficiency across the data stack, with deep knowledge of Python, SQL, and ETL methodologies.
• Hands-on familiarity with the big data ecosystem, including technologies such as Trino, Kafka, and Iceberg.
• A growth mindset characterized by curiosity and a proactive approach to solving complex architectural problems.
• Strong collaborative communication skills, with a proven ability to align and work effectively across diverse technical stakeholder groups.
• We are primarily an in-office environment and therefore, you will be expected to work from the Prague office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.

#LIONSITE

THE ROLE

As a Data Engineer - Agentic AI focus you will architect the high-performance data ecosystem that transforms raw information into actionable intelligence and agentic AI solutions. You will bridge the gap between complex engineering and business impact, collaborating with Data Scientists and Product Managers to build the infrastructure that scales our AI capabilities. This isn't just about moving data; it’s about engineering the foundation for the next generation of automated customer support and operational excellence.

WHAT YOU'LL DO

• Architect Scalable Data Infrastructure: Design, deploy, and optimize production-grade ETL pipelines and data models that ensure high-quality, AI-ready datasets for global business intelligence.
• Drive AI Evolution: Lead the technical preparation and prototyping of AI-driven workflows, integrating emerging LLM frameworks to automate complex business processes and enhance decision-making.
• Ensure Data Excellence and Integrity: Own the end-to-end data management lifecycle, implementing robust governance, security, and monitoring to maintain a "single source of truth" for critical stakeholders.
• Collaborate and Solve Across Functions: Partner with cross-functional teams to translate business needs into technical specifications, serving as the bridge that turns raw data into measurable organizational impact.
• Operational Reliability: Manage business-hour on-call rotations to ensure the seamless performance of ingestion systems, maintaining high availability for our global data consumers.

WHAT YOU BRING

• Advanced Engineering Proficiency: Deep technical expertise in SQL, Python, and data modeling, with a proven track record of maintaining complex data pipelines and workflow orchestration tools like Airflow or DBT.
• Modern Data Stack Expertise: Hands-on experience managing relational and NoSQL environments (such as Snowflake, PostgreSQL, or MongoDB) within AWS cloud infrastructures.
• Growth Mindset & AI Curiosity: A strong desire to apply traditional data engineering principles to intelligent automation, including the ability to adapt to new technologies like LLM orchestration and model evaluation.
• Collaborative Communication: The ability to distill complex technical concepts into clear insights for non-technical partners, ensuring alignment across global time zones and diverse teams.
• We are primarily an in-office environment and therefore, you will be expected to work from the Prague, Czech Republic office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.

#LIONSITE

Become a Senior SRE for Bloomreach!

Join the newly form Datacraft team — the team building the next-generation data platform for Bloomreach Engagement. Datacraft owns three interconnected domains:

• Data Warehouses (~60%) — making Bloomreach data first-class in customer DWHs (Snowflake, BigQuery, Databricks). The strategic goal for 2026–27 is to use DWHs to exponentially accelerate data adoption.
• Loomi Analytics Agent (~20%) — evolving Loomi Analytics into an agentic analytics assistant that can explore data across systems, explain insights, and act on them.
• Dashboards & Analytics Stack (~20%) — moving Engagement reporting onto DWH-backed, modern analytics stacks (semantic layers, headless BI tools).

As a Senior SRE, you will be the reliability backbone of this AI-first data team. Your work will directly impact the deployments, pipelines, reliability, and observability of pipelines and services that hundreds of enterprise customers depend on — from data exports into Databricks and BigQuery, to the  AI agent Loomi uses to surface insights.

Datacraft is an AI-first team. We believe code is a commodity and expect every engineer to fluently use coding agents (e.g., Cursor, Claude Code, Copilot, Gemini CLI) as a core part of their daily workflow. The ability to leverage AI tooling to accelerate development, prototyping, and problem-solving is not optional — it's foundational. Working in one of our Central European offices (Bratislava, Praha, Brno) or from home on a full-time basis, you'll become a core part of the Engineering team.

What challenge awaits you?

As a P3 (Senior) SRE at Bloomreach, you are an independent professional — expert in reliability engineering, able to decompose objectives into actionable infrastructure improvements, and lead initiatives end-to-end with minimal day-to-day guidance.

We need you to build and operate an ecosystem where data engineers can safely and efficiently develop, debug, and operate data-intensive jobs and services — spanning Kafka ingest pipelines, Iceberg data lakes, multi-DWH exports, Databricks deployment and orchestration (Airflow / Cloud Composer), and agentic AI workloads.

Your responsibilities

a. Platform reliability & observability

• Build and maintain the reliability ecosystem where engineers can safely develop, debug, and operate DataCraft services running on GCP and Kubernetes (DataProc, Cloud Composer, BigQuery, Snowflake/Databricks connectors).
• Ensure end-to-end observability across the full data platform — from Kafka ingest through GCS/Iceberg staging, Airflow orchestration, to Databricks and BigQuery destinations — enabling the team to catch missing loads, SLA breaches, and data drifts before customers notice, or costs drift.
• Drive scalability so services can scale vertically and horizontally based on operational and telemetric data (OpenTelemetry, Prometheus, Victoria Metrics).
• Maintain team health dashboards and alerting (Grafana, PagerDuty, Sentry).

b. Infrastructure as Code & deployments

• Own and evolve Terraform-based infrastructure for DataCraft services.
• Automate deployments, instance setup, and operational runbooks to eliminate manual/semi-manual steps.
• Maintain CI/CD pipelines (GitLab) with linters, security scans, and code quality checks, AI code reviews, enabling engineers to produce high-quality MRs.

c. Security & compliance

• Help the team fulfill security requirements for ISO and SOC2 audits by enforcing security principles: key distribution, key rotation, authorization & authentication at the service level, data encryption in transit, data isolation, resource limitations, and audit logs.
• Ensure data access controls are properly enforced across multi-DWH environments (BigQuery, Snowflake, Databricks).

d. Incident management & L3 support

• Participate in and drive L3 on-call rotation and incident resolution for DataCraft services.
• Contribute tooling for debugging, troubleshooting, and performance testing of data pipelines and orchestration layers.
• Use telemetry data and distributed tracing to navigate complex, distributed service architectures.

e. Agentic platform reliability

• Ensure reliability and observability of the Loomi Analytics Agent data infrastructure — LLM API gateway performance, MCP server health, and evaluation pipeline availability.
• Monitor and alert on data quality issues that could introduce inconsistencies or hallucinations in Loomi's responses — making the agent's data access patterns reliable and debuggable.

Our tech stack

Languages: Python (primary), Go, SQL Messaging & streaming: Apache Kafka Storage & databases: Databricks, BigQuery, Apache Iceberg, GCS, Mongo, Redis Data processing & orchestration: Apache Spark, DataFlow, Airflow / Cloud Composer Infrastructure: GCP, Kubernetes, Terraform AI / Agentic: LLM APIs, MCP, agent orchestration frameworks Observability: Grafana, Prometheus, Victoria Metrics, PagerDuty, Sentry, OpenTelemetry CI/CD & tooling: GitLab, Jira, Confluence AI coding agents: Cursor, Claude Code

Your qualifications

Professional experience

Impact

• You can articulate how your contributions transformed the way engineers work and fostered a strong SRE/DevOps culture.
• You can demonstrate how impactful reliability work connects to business success and customer outcomes.

Ownership

• You embrace the you build it, you run it principle — you love owning what you ship.
• You are cost-aware: effective vertical and horizontal autoscaling and detailed telemetry insights are how you demonstrate mindfulness of cloud spend.

Systematic approach

• Infrastructure as Code is the only thing that brings stability into chaos
• You design for failure: SLOs, error budgets, and runbooks are first-class artifacts, not afterthoughts.

Data-driven

• You use telemetry and metrics to give engineers actionable feedback on how applications and services behave.
• You can navigate complex data platform architectures using distributed tracing and debugging.

Technical skills

• Solid hands-on experience with GCP (BigQuery, DataProc, Cloud Composer, GCS) and Kubernetes.
• Experience with Python; Go is a strong advantage.
• Familiarity with data pipeline technologies (Kafka, Airflow/Cloud Composer, Spark, Iceberg) — you don't need to write ETL code, but you need to operate it reliably and know when something is wrong.
• Fluent use of AI coding agents (Cursor, Claude Code, Copilot, Gemini CLI, or similar) — you already use these tools daily to accelerate work.
• Comfortable with on-call rotation and 24/7 incident response.
• Remote-first mindset — you know how to be effective in distributed teams.
• You are able to learn and adapt — essential when exploring new tech or navigating our growing codebase.

Strongly preferred

• Experience operating single-DWH environments (Snowflak, Databricks or BigQuery).
• Familiarity with agentic/LLM workloads — API reliability, latency SLOs, trace observability for AI systems.
• Experience with open table formats (Iceberg, Delta Lake) in production environments.
• Exposure to data security and compliance in the context of customer-facing DWH integrations (consent, data retention, PII handling).

Personal qualities

• Ownership & accountability — you take issues from detection through to resolution and follow-up prevention.
• Systematic thinking — you identify root causes, not symptoms, and document your findings so the team learns.
• Collaboration & communication — you explain trade-offs and constraints clearly to both engineers and non-engineers.
• Bias for reliability — operational excellence (SLOs, oncall friendliness, proactive alerting) is not a chore, it's your craft.
• Continuous improvement mindset — you are comfortable iterating, revisiting assumptions, and improving incrementally.
• Comfortable operating remote-first in a distributed team across Central Europe.

Your success story

In 30 days:

• Get to know the DataCraft team, the company, and the most important processes.
• Set up your local and GCP development environment and complete the Engagement engineering onboarding.
• Understand the current state of DataCraft services: pipelines, orchestration, observability gaps, and on-call runbooks.

In 90 days:

• Start contributing to the L3 on-call rotation, handling incidents, troubleshooting, and debugging — which will sharpen your understanding of the platform and surface fresh improvement ideas.
• Deliver your first meaningful reliability improvement: an observability enhancement, a deployment automation, or an SLO definition for a key DataCraft service.

In 180 days:

• Own the reliability posture of at least one DataCraft domain end-to-end — able to independently design, operate, and continuously improve it.
• Drive measurable improvements in MTTR, alert signal-to-noise ratio, or deployment confidence across the team.
• Be a trusted reliability partner in architecture discussions — your input shapes how new DataCraft services are designed for operability from day one.

#LI-KP1

Become a Senior SRE for Bloomreach!

Join the newly form Datacraft team — the team building the next-generation data platform for Bloomreach Engagement. Datacraft owns three interconnected domains:

• Data Warehouses (~60%) — making Bloomreach data first-class in customer DWHs (Snowflake, BigQuery, Databricks). The strategic goal for 2026–27 is to use DWHs to exponentially accelerate data adoption.
• Loomi Analytics Agent (~20%) — evolving Loomi Analytics into an agentic analytics assistant that can explore data across systems, explain insights, and act on them.
• Dashboards & Analytics Stack (~20%) — moving Engagement reporting onto DWH-backed, modern analytics stacks (semantic layers, headless BI tools).

As a Senior SRE, you will be the reliability backbone of this AI-first data team. Your work will directly impact the deployments, pipelines, reliability, and observability of pipelines and services that hundreds of enterprise customers depend on — from data exports into Databricks and BigQuery, to the  AI agent Loomi uses to surface insights.

Datacraft is an AI-first team. We believe code is a commodity and expect every engineer to fluently use coding agents (e.g., Cursor, Claude Code, Copilot, Gemini CLI) as a core part of their daily workflow. The ability to leverage AI tooling to accelerate development, prototyping, and problem-solving is not optional — it's foundational. 

For candidates at the P3 / Senior SRE level, starting monthly compensation begins at 3 800 € gross, with the final offer tailored for each candidate based on their skills and experience. Stock options and a comprehensive benefits package are also included. Working in one of our Central European offices (Bratislava, Praha, Brno) or from home on a full-time basis, you'll become a core part of the Engineering team.

What challenge awaits you?

As a P3 (Senior) SRE at Bloomreach, you are an independent professional — expert in reliability engineering, able to decompose objectives into actionable infrastructure improvements, and lead initiatives end-to-end with minimal day-to-day guidance.

We need you to build and operate an ecosystem where data engineers can safely and efficiently develop, debug, and operate data-intensive jobs and services — spanning Kafka ingest pipelines, Iceberg data lakes, multi-DWH exports, Databricks deployment and orchestration (Airflow / Cloud Composer), and agentic AI workloads.

Your responsibilities

a. Platform reliability & observability

• Build and maintain the reliability ecosystem where engineers can safely develop, debug, and operate DataCraft services running on GCP and Kubernetes (DataProc, Cloud Composer, BigQuery, Snowflake/Databricks connectors).
• Ensure end-to-end observability across the full data platform — from Kafka ingest through GCS/Iceberg staging, Airflow orchestration, to Databricks and BigQuery destinations — enabling the team to catch missing loads, SLA breaches, and data drifts before customers notice, or costs drift.
• Drive scalability so services can scale vertically and horizontally based on operational and telemetric data (OpenTelemetry, Prometheus, Victoria Metrics).
• Maintain team health dashboards and alerting (Grafana, PagerDuty, Sentry).

b. Infrastructure as Code & deployments

• Own and evolve Terraform-based infrastructure for DataCraft services.
• Automate deployments, instance setup, and operational runbooks to eliminate manual/semi-manual steps.
• Maintain CI/CD pipelines (GitLab) with linters, security scans, and code quality checks, AI code reviews, enabling engineers to produce high-quality MRs.

c. Security & compliance

• Help the team fulfill security requirements for ISO and SOC2 audits by enforcing security principles: key distribution, key rotation, authorization & authentication at the service level, data encryption in transit, data isolation, resource limitations, and audit logs.
• Ensure data access controls are properly enforced across multi-DWH environments (BigQuery, Snowflake, Databricks).

d. Incident management & L3 support

• Participate in and drive L3 on-call rotation and incident resolution for DataCraft services.
• Contribute tooling for debugging, troubleshooting, and performance testing of data pipelines and orchestration layers.
• Use telemetry data and distributed tracing to navigate complex, distributed service architectures.

e. Agentic platform reliability

• Ensure reliability and observability of the Loomi Analytics Agent data infrastructure — LLM API gateway performance, MCP server health, and evaluation pipeline availability.
• Monitor and alert on data quality issues that could introduce inconsistencies or hallucinations in Loomi's responses — making the agent's data access patterns reliable and debuggable.

Our tech stack

Languages: Python (primary), Go, SQL Messaging & streaming: Apache Kafka Storage & databases: Databricks, BigQuery, Apache Iceberg, GCS, Mongo, Redis Data processing & orchestration: Apache Spark, DataFlow, Airflow / Cloud Composer Infrastructure: GCP, Kubernetes, Terraform AI / Agentic: LLM APIs, MCP, agent orchestration frameworks Observability: Grafana, Prometheus, Victoria Metrics, PagerDuty, Sentry, OpenTelemetry CI/CD & tooling: GitLab, Jira, Confluence AI coding agents: Cursor, Claude Code

Your qualifications

Professional experience

Impact

• You can articulate how your contributions transformed the way engineers work and fostered a strong SRE/DevOps culture.
• You can demonstrate how impactful reliability work connects to business success and customer outcomes.

Ownership

• You embrace the you build it, you run it principle — you love owning what you ship.
• You are cost-aware: effective vertical and horizontal autoscaling and detailed telemetry insights are how you demonstrate mindfulness of cloud spend.

Systematic approach

• Infrastructure as Code is the only thing that brings stability into chaos
• You design for failure: SLOs, error budgets, and runbooks are first-class artifacts, not afterthoughts.

Data-driven

• You use telemetry and metrics to give engineers actionable feedback on how applications and services behave.
• You can navigate complex data platform architectures using distributed tracing and debugging.

Technical skills

• Solid hands-on experience with GCP (BigQuery, DataProc, Cloud Composer, GCS) and Kubernetes.
• Experience with Python; Go is a strong advantage.
• Familiarity with data pipeline technologies (Kafka, Airflow/Cloud Composer, Spark, Iceberg) — you don't need to write ETL code, but you need to operate it reliably and know when something is wrong.
• Fluent use of AI coding agents (Cursor, Claude Code, Copilot, Gemini CLI, or similar) — you already use these tools daily to accelerate work.
• Comfortable with on-call rotation and 24/7 incident response.
• Remote-first mindset — you know how to be effective in distributed teams.
• You are able to learn and adapt — essential when exploring new tech or navigating our growing codebase.

Strongly preferred

• Experience operating single-DWH environments (Snowflak, Databricks or BigQuery).
• Familiarity with agentic/LLM workloads — API reliability, latency SLOs, trace observability for AI systems.
• Experience with open table formats (Iceberg, Delta Lake) in production environments.
• Exposure to data security and compliance in the context of customer-facing DWH integrations (consent, data retention, PII handling).

Personal qualities

• Ownership & accountability — you take issues from detection through to resolution and follow-up prevention.
• Systematic thinking — you identify root causes, not symptoms, and document your findings so the team learns.
• Collaboration & communication — you explain trade-offs and constraints clearly to both engineers and non-engineers.
• Bias for reliability — operational excellence (SLOs, oncall friendliness, proactive alerting) is not a chore, it's your craft.
• Continuous improvement mindset — you are comfortable iterating, revisiting assumptions, and improving incrementally.
• Comfortable operating remote-first in a distributed team across Central Europe.

Your success story

In 30 days:

• Get to know the DataCraft team, the company, and the most important processes.
• Set up your local and GCP development environment and complete the Engagement engineering onboarding.
• Understand the current state of DataCraft services: pipelines, orchestration, observability gaps, and on-call runbooks.

In 90 days:

• Start contributing to the L3 on-call rotation, handling incidents, troubleshooting, and debugging — which will sharpen your understanding of the platform and surface fresh improvement ideas.
• Deliver your first meaningful reliability improvement: an observability enhancement, a deployment automation, or an SLO definition for a key DataCraft service.

In 180 days:

• Own the reliability posture of at least one DataCraft domain end-to-end — able to independently design, operate, and continuously improve it.
• Drive measurable improvements in MTTR, alert signal-to-noise ratio, or deployment confidence across the team.
• Be a trusted reliability partner in architecture discussions — your input shapes how new DataCraft services are designed for operability from day one.

#LI-KP1

Shape the Future of Financial Services with Capco

At Capco, we specialize in management consulting and software development, partnering with leading international banks and insurance companies across Western Europe. Our innovative approach and collaborative culture make us the ideal place for professionals looking to make an impact in financial services technology.

Want to learn more about our culture and people? Visit us: Capco Careers Slovakia

Why Join Us?

• Continuous Learning & Development – Access to technical and soft skills training, professional certifications, and full access to the Udemy online learning platform.
• Flexible Work Environment – we provide flexibility to work remotely from the home office within Czech Republic
• Attractive Benefits Package:
  ✅ Paid sick days
  ✅ 25 vacation days (regardless of age)
  ✅ Cafeteria provided by the Benefits plus services
  ✅ Fully paid meal vouchers
  ✅ Smartphone with 1TB data + Laptop
• Well-being & Community Engagement – mental health webinars, running challenges, sports activities, multisport card or participate in our volunteering days.
• Long-term career – Indefinite period contract with opportunities for career growth.

About the Role

We are building a modern enterprise data platform to support our next phase of digital transformation, including SAP integration, internal application evolution, and advanced BI capabilities.

We are seeking a Data Platform & Architecture Lead to design, own, and evolve how data flows across our organization. This role is focused on data architecture, integration, modeling and database administration.

You will play a critical role in establishing a scalable, well-governed data foundation that enables consistent reporting, operational insights, and future AI capabilities.

What You’ll Do

1. Own Enterprise Data Architecture

• Design and maintain the end-to-end data architecture across systems
• Define canonical data models that unify data from SAP, internal applications, and financial systems
• Establish standards for data structure, naming, and consistency

2. Design Data Pipelines & Orchestration

• Architect and oversee data pipelines and transformation layers
• Work with our internal ETL/orchestration platform to manage data flows
• Ensure scalability, reliability, and observability of pipelines

3. Lead Multi-System Integration

• Integrate data across:

• SAP
• Internal applications (Sales, Performance, Resourcing)
• Financial planning tools (e.g., Anaplan)

• Resolve inconsistencies and define a single source of truth

4. Enable BI & Analytics

• Design analytics-ready data models (fact/dimension structures)
• Partner with BI teams (Looker) to ensure consistent, scalable reporting
• Support metric standardization across the organization

5. Drive Data Strategy & Maturity

• Assess current data landscape and define a data maturity roadmap
• Introduce best practices in:

• Data governance
• Data quality
• Data lifecycle management

6. Collaborate Across Teams

• Work closely with Engineering, BI, and Business stakeholders
• Translate business requirements into scalable data solutions
• Act as a bridge between technical and functional teams

What We’re Looking For

Must-Have Experience

• Proven experience in Data Architecture or Senior Data Engineering roles
• Strong background in designing end-to-end data pipelines
• Experience with data modeling for analytics (fact/dimension, semantic layers)
• Experience integrating multiple systems (ERP, APIs, internal platforms)
• Proficiency in SQL and Python (or similar modern data tooling)
• Solid understanding of data warehousing and modern data architectures

Nice to Have

• Experience with orchestration tools (e.g., Airflow, Dagster, or similar)
• Familiarity with columnar databases (e.g., ClickHouse, Snowflake, BigQuery)
• Experience working with BI tools such as Looker
• Exposure to API-based data ingestion and semi-structured data (JSON)

How to Stand Out

Candidates who stand out will be able to clearly articulate:

• A data platform or architecture they have designed
• How they integrated multiple systems into a unified model
• How they enabled BI or analytics through data modeling

Your Hiring Journey

✅ Screening Call – Introductory chat with our recruiter
✅ Technical Interviews (1-2 rounds, depending on the project) – Discuss your skills with our experienced Capco colleagues
✅ Feedback & Offer – Receive an offer to join our team!

Why Capco? Because You Matter.

At Capco, we prioritize your career growth and well-being. You will have a personal coach to support your development and a flat hierarchy that encourages you to share and implement your ideas. Plus, our team events, sports activities, and networking opportunities help you stay connected and engaged beyond work.

Curious to learn more? Apply now – it only takes 5 minutes!

📩 Contact: Tomas Vanco – tomas.vanco@capco.com
💻 Connect on LinkedIn: Tomas Vanco | LinkedIn

#fulltime 

Shape the Future of Financial Services with Capco

At Capco, we specialize in management consulting and software development, partnering with leading international banks and insurance companies across Western Europe. Our innovative approach and collaborative culture make us the ideal place for professionals looking to make an impact in financial services technology.

Want to learn more about our culture and people? Visit us: Capco Careers Slovakia

Why Join Us?

• Continuous Learning & Development – Access to technical and soft skills training, professional certifications, and full access to the Udemy online learning platform.
• Flexible Work Environment – we provide flexibility to work remotely from the home office within Czech Republic
• Attractive Benefits Package:
  ✅ Paid sick days
  ✅ 25 vacation days (regardless of age)
  ✅ Cafeteria provided by the Benefits plus services
  ✅ Fully paid meal vouchers
  ✅ Smartphone with 1TB data + Laptop
• Well-being & Community Engagement – mental health webinars, running challenges, sports activities, multisport card or participate in our volunteering days.
• Long-term career – Indefinite period contract with opportunities for career growth.

About the Role

We are building a modern enterprise data platform to support our next phase of digital transformation, including SAP integration, internal application evolution, and advanced BI capabilities.

We are seeking a Data Platform & Architecture Lead to design, own, and evolve how data flows across our organization. This role is focused on data architecture, integration, modeling and database administration.

You will play a critical role in establishing a scalable, well-governed data foundation that enables consistent reporting, operational insights, and future AI capabilities.

What You’ll Do

1. Own Enterprise Data Architecture

• Design and maintain the end-to-end data architecture across systems
• Define canonical data models that unify data from SAP, internal applications, and financial systems
• Establish standards for data structure, naming, and consistency

2. Design Data Pipelines & Orchestration

• Architect and oversee data pipelines and transformation layers
• Work with our internal ETL/orchestration platform to manage data flows
• Ensure scalability, reliability, and observability of pipelines

3. Lead Multi-System Integration

• Integrate data across:

• SAP
• Internal applications (Sales, Performance, Resourcing)
• Financial planning tools (e.g., Anaplan)

• Resolve inconsistencies and define a single source of truth

4. Enable BI & Analytics

• Design analytics-ready data models (fact/dimension structures)
• Partner with BI teams (Looker) to ensure consistent, scalable reporting
• Support metric standardization across the organization

5. Drive Data Strategy & Maturity

• Assess current data landscape and define a data maturity roadmap
• Introduce best practices in:

• Data governance
• Data quality
• Data lifecycle management

6. Collaborate Across Teams

• Work closely with Engineering, BI, and Business stakeholders
• Translate business requirements into scalable data solutions
• Act as a bridge between technical and functional teams

What We’re Looking For

Must-Have Experience

• Proven experience in Data Architecture or Senior Data Engineering roles
• Strong background in designing end-to-end data pipelines
• Experience with data modeling for analytics (fact/dimension, semantic layers)
• Experience integrating multiple systems (ERP, APIs, internal platforms)
• Proficiency in SQL and Python (or similar modern data tooling)
• Solid understanding of data warehousing and modern data architectures

Nice to Have

• Experience with orchestration tools (e.g., Airflow, Dagster, or similar)
• Familiarity with columnar databases (e.g., ClickHouse, Snowflake, BigQuery)
• Experience working with BI tools such as Looker
• Exposure to API-based data ingestion and semi-structured data (JSON)

How to Stand Out

Candidates who stand out will be able to clearly articulate:

• A data platform or architecture they have designed
• How they integrated multiple systems into a unified model
• How they enabled BI or analytics through data modeling

Your Hiring Journey

✅ Screening Call – Introductory chat with our recruiter
✅ Technical Interviews (1-2 rounds, depending on the project) – Discuss your skills with our experienced Capco colleagues
✅ Feedback & Offer – Receive an offer to join our team!

Why Capco? Because You Matter.

At Capco, we prioritize your career growth and well-being. You will have a personal coach to support your development and a flat hierarchy that encourages you to share and implement your ideas. Plus, our team events, sports activities, and networking opportunities help you stay connected and engaged beyond work.

Curious to learn more? Apply now – it only takes 5 minutes!

📩 Contact: Tomas Vanco – tomas.vanco@capco.com
💻 Connect on LinkedIn: Tomas Vanco | LinkedIn

#fulltime 

Shape the Future of Financial Services with Capco

At Capco, we specialize in management consulting and software development, partnering with leading international banks and insurance companies across Western Europe. Our innovative approach and collaborative culture make us the ideal place for professionals looking to make an impact in financial services technology.

Want to learn more about our culture and people? Visit us: Capco Careers Slovakia

Why Join Us?

• Continuous Learning & Development – Access to technical and soft skills training, professional certifications, and full access to the Udemy online learning platform.
• Flexible Work Environment – we provide flexibility to work remotely from the home office within Czech Republic
• Attractive Benefits Package:
  ✅ Paid sick days
  ✅ 25 vacation days (regardless of age)
  ✅ Cafeteria provided by the Benefits plus services
  ✅ Fully paid meal vouchers
  ✅ Smartphone with 1TB data + Laptop
• Well-being & Community Engagement – mental health webinars, running challenges, sports activities, multisport card or participate in our volunteering days.
• Long-term career – Indefinite period contract with opportunities for career growth.

About the Role

We are building a modern enterprise data platform to support our next phase of digital transformation, including SAP integration, internal application evolution, and advanced BI capabilities.

We are seeking a Data Platform & Architecture Lead to design, own, and evolve how data flows across our organization. This role is focused on data architecture, integration, modeling and database administration.

You will play a critical role in establishing a scalable, well-governed data foundation that enables consistent reporting, operational insights, and future AI capabilities.

What You’ll Do

1. Own Enterprise Data Architecture

• Design and maintain the end-to-end data architecture across systems
• Define canonical data models that unify data from SAP, internal applications, and financial systems
• Establish standards for data structure, naming, and consistency

2. Design Data Pipelines & Orchestration

• Architect and oversee data pipelines and transformation layers
• Work with our internal ETL/orchestration platform to manage data flows
• Ensure scalability, reliability, and observability of pipelines

3. Lead Multi-System Integration

• Integrate data across:

• SAP
• Internal applications (Sales, Performance, Resourcing)
• Financial planning tools (e.g., Anaplan)

• Resolve inconsistencies and define a single source of truth

4. Enable BI & Analytics

• Design analytics-ready data models (fact/dimension structures)
• Partner with BI teams (Looker) to ensure consistent, scalable reporting
• Support metric standardization across the organization

5. Drive Data Strategy & Maturity

• Assess current data landscape and define a data maturity roadmap
• Introduce best practices in:

• Data governance
• Data quality
• Data lifecycle management

6. Collaborate Across Teams

• Work closely with Engineering, BI, and Business stakeholders
• Translate business requirements into scalable data solutions
• Act as a bridge between technical and functional teams

What We’re Looking For

Must-Have Experience

• Proven experience in Data Architecture or Senior Data Engineering roles
• Strong background in designing end-to-end data pipelines
• Experience with data modeling for analytics (fact/dimension, semantic layers)
• Experience integrating multiple systems (ERP, APIs, internal platforms)
• Proficiency in SQL and Python (or similar modern data tooling)
• Solid understanding of data warehousing and modern data architectures

Nice to Have

• Experience with orchestration tools (e.g., Airflow, Dagster, or similar)
• Familiarity with columnar databases (e.g., ClickHouse, Snowflake, BigQuery)
• Experience working with BI tools such as Looker
• Exposure to API-based data ingestion and semi-structured data (JSON)

How to Stand Out

Candidates who stand out will be able to clearly articulate:

• A data platform or architecture they have designed
• How they integrated multiple systems into a unified model
• How they enabled BI or analytics through data modeling

Your Hiring Journey

✅ Screening Call – Introductory chat with our recruiter
✅ Technical Interviews (1-2 rounds, depending on the project) – Discuss your skills with our experienced Capco colleagues
✅ Feedback & Offer – Receive an offer to join our team!

Why Capco? Because You Matter.

At Capco, we prioritize your career growth and well-being. You will have a personal coach to support your development and a flat hierarchy that encourages you to share and implement your ideas. Plus, our team events, sports activities, and networking opportunities help you stay connected and engaged beyond work.

Curious to learn more? Apply now – it only takes 5 minutes!

📩 Contact: Tomas Vanco – tomas.vanco@capco.com
💻 Connect on LinkedIn: Tomas Vanco | LinkedIn

#fulltime 

The role 

The Data Engineering team is responsible for building and maintaining robust data infrastructure that powers analytics, and business intelligence across Nebius. We design and implement scalable data pipelines, optimize data storage and processing, and enable data-driven decision making across the organization. The team works closely with product teams and business stakeholders to ensure alignment with company goals.We are looking for a Data Engineer to design, build, and maintain our data infrastructure and pipelines. You will work on processing large-scale datasets, optimizing data workflows, and enabling analytics capabilities that support our rapidly growing cloud platform.

Your responsibilities: 

• Design, develop, and maintain scalable data pipelines.
• Build and optimize data infrastructure.
• Implement data quality monitoring and validation frameworks.
• Optimize data storage, processing, and query performance for large-scale datasets.
• Design and implement data models for analytics and reporting use cases.
• Develop tools and automation to improve data engineering workflows and productivity.
• Ensure data governance, security, and compliance standards are met.
• Participate in on-call rotation to support production data systems.

Must-haves: 

• 3+ years of experience in data engineering or related roles.
• Experience building and maintaining data pipelines using orchestration tools (e.g., Airflow, Prefect, Dagster).
• Strong proficiency in SQL and solid programming skills in Python.
• Experience with distributed data processing frameworks (e.g., Apache Spark, or similar).
• Knowledge of data modeling principles and best practices.
• Understanding of data architectures and storage systems.

Nice-to-haves: 

• Experience with real-time data streaming platforms.
• Familiarity with Infrastructure as Code tools (Terraform, etc).
• Experience with containerization (Docker) and Kubernetes.
• Knowledge of data governance and privacy frameworks (GDPR, SOC2).
• Knowledge of data quality and observability tools (Great Expectations, etc.).

We conduct coding interviews as part of the process.

The role

We’re looking for a Software Engineer with strong C++ expertise to join the team building and operating Nebius Data Platform — a distributed storage and a processing platform that acts as the company’s “source of truth” and the backbone of many internal (and some external) products.

Nebius Data Platform is a single multi-tenant ecosystem based on YTsaurus — instead of running separate HDFS/Kafka/HBase-style systems, we provide storage, compute, and analytics capabilities inside one platform.

Built on top of the open-source YTsaurus ecosystem, we run and extend our own Nebius distribution and develop significant in-house functionality (core and platform-level). We can design, implement, and roll out features end-to-end on our clusters without waiting for upstream approvals and contribute upstream when it makes sense.

At scale today, this includes~500 servers, ~20k CPU cores and ~10 PB of compressed data in our largest production cluster, supporting workloads ranging from business-critical pipelines and financial transactions to large-scale ML/LLM training datasets and compute.

What’s inside the platform

You’ll work on a system that includes (and ties together):

• Distributed Storage (Cypress): transactional semantics, tiered storage, erasure coding, replication, and strong reliability expectations.
• Compute & ETL: a cluster-wide job scheduler (tens of thousands of cores), MapReduce, YQL for SQL-like data processing, and SPYT (Spark over YTsaurus) for modern data engineering.
• Interactive analytics (CHYT): ClickHouse® instances spun up directly on compute nodes for fast SQL over data in-place.
• Dynamic Tables: low-latency NoSQL KV with distributed ACID transactions for OLTP-style workloads and feature stores.
• Orchestracto: workflow orchestration deeply integrated with the platform (Airflow-like, but platform-native).

What you’ll do

We’re looking for engineers who combine strong systems skills with product sense: understanding who uses the platform, why certain capabilities matter, and making pragmatic trade-offs to maximize impact. On our team, engineering work is expected to be connected to real users and outcomes — you’ll regularly align with internal stakeholders, clarify requirements, and help drive prioritization.

In this role, you will:

• Design and implement new functionality in YTsaurus core (C++) with production reliability in mind.
• Build and evolve platform-level capabilities: platform architecture and operating model—multi-cluster growth, shared primitives, and a consistent experience that scales with new teams and use cases.
• Improve end-to-end platform experience for internal (and external-facing) users: APIs, guardrails, debugging workflows, and automation.
• Own production quality: incident response / on-call rotation, root cause analysis, and turning learnings into durable fixes.

Example projects

• Roll out sharded YTsaurus masters (incl. Kubernetes operator support) and build automatic balancing of metadata across master cells (consensus groups) to remove control-plane bottlenecks and unlock 10–100x cluster growth.
• Make CHYT interactive SQL faster and more predictable at high load via performance work like data-skipping / min-max-style indexes and improved execution introspection.
• Turn Orchestracto into a platform product by defining the building blocks, developer experience, and governance for how teams create and share workflows.
• Scale and harden Parquet-on-S3 for native YTsaurus workloads by tackling replication/movement, consistent lifecycle semantics, and master-server metadata optimizations for performance and reliability.
• Design and ship complete, trustworthy audit trails for data changes (who/what/when) across heterogeneous storage and compute paths.

Tech stack

• Core: modern C++ (C++20, async + multithreaded primitives)
• Services & tooling: Go and Python (microservices, utilities, integration tests)

What we expect

• 5+ years of software engineering experience.
• Strong C++ skills (you’ll write core code).
• Working knowledge of Python and/or Go (you don’t have to be expert, but should be comfortable navigating them).
• Experience developing and/or operating high-load, distributed services.
• Production mindset: ability to use SSH, read logs/metrics/traces, and debug distributed systems behavior.
• Solid CS fundamentals: algorithms, data structures, concurrency basics.

Nice to have

• Experience with Big Data systems (YTsaurus/Hadoop/Spark/ClickHouse/Kafka-like ecosystems).
• Experience with multi-tenant platforms, schedulers, resource isolation, quotas, and reliability engineering.
• Strong performance engineering skills (profiling, lock contention, latency/throughput tradeoffs).

We conduct coding interviews as part of the process.

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive.

By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.

As part of our team, you will:

• Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
• Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
• Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
• Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
• Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.

We’ll be happy to have you on our team if you have:

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• A solid understanding of the ML lifecycle from idea to the customer-facing application.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
• Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
• At least three years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow.
• Developing infrastructure components and services using cluster solutions like Kubernetes.
• The development of Python-based backend services.
• Creating and maintaining ML pipelines, including legacy ones.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
• Writing and maintaining Python libraries used by internal (or external) ML engineers.
• A strong theoretical background in NLP and transformer-based approaches.
• Writing code in Java and/or Kotlin.

#LI-HYBRID
#LI-MR1

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.

As part of our team, you will:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#LI-KP1