Jobs Data Engineer

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

Our client is a leading online gaming operator developing a new-generation transactional platform for user account management. This greenfield solution is being built for a highly regulated, high-traffic environment and is expected to support millions of concurrent users.

The platform will handle real-time financial transactions and requires strong availability, sub-second performance, data integrity, security, and complete auditability. A major engineering challenge is ensuring the system remains reliable and performant during significant traffic surges and peak events.

The team is looking for a hands-on MLOps Engineer who will contribute to designing and building scalable, reliable ML infrastructure, creating and maintaining production-grade data and ML pipelines, and supporting the complete ML lifecycle. Particular attention will be given to data quality, monitoring, governance, and overall system reliability.
 

About the Role:

We are looking for a Senior MLOps Engineer to build and evolve scalable cloud infrastructure, real-time data pipelines, and production ML workflows.

You will work with Infrastructure as Code, event-driven systems, and end-to-end ML processes, covering model deployment, monitoring, data governance, and reliability. Working closely with Data Engineers and Data Scientists, you will help shape the new platform, ensure reliable data and ML workflows from end to end, and understand the impact of upstream data changes on downstream models.

This position offers significant technical ownership and the opportunity to contribute to architectural decisions, technology selection, and the long-term evolution of the platform.
 

Key Responsibilities:

• Design, provision, and maintain scalable cloud infrastructure using Infrastructure as Code.
• Develop and operate robust real-time, event-driven data pipelines with fault tolerance and sub-second latency.
• Design and optimize storage systems and data lake infrastructure supporting both transactional and analytical workloads.
• Build high-availability networking solutions, API gateways, automated failover mechanisms, and zero-downtime routing.
• Embed data validation, observability, and monitoring into pipelines to maintain reliability and quickly detect potential issues.
• Build and maintain infrastructure supporting ML model training, validation, deployment, and inference.
• Automate deployment and testing processes for containerized workloads running on Kubernetes.
• Partner with cross-functional teams to establish ML governance practices and dependable end-to-end data flows.
• Contribute to long-term cloud scalability, FinOps and cost optimization initiatives, as well as disaster recovery planning.
• Provide technical guidance, contribute to architecture reviews, and mentor team members on engineering best practices.
   

Required Competence and Skills:

• 5+ years of experience in cloud infrastructure, data engineering, or distributed systems architecture.
• Strong hands-on experience with infrastructure provisioning tools such as Terraform, container technologies including Docker and Kubernetes, and cloud environments such as AWS or GCP.
• Practical experience with distributed streaming technologies such as Apache Kafka and Change Data Capture (CDC) approaches.
• Strong knowledge of object storage architectures such as S3, distributed data lakes, and SQL/NoSQL databases.
• Strong Python skills and scripting experience, including Bash, for automation and pipeline orchestration.
• Good understanding of ML workflows, data governance, and model lifecycle management.
• Ability to turn high-level architectural concepts into practical implementations and optimize them for performance.
• Strong communication skills and experience working closely with Data Scientists and Engineering teams.
   

Nice to Have:

• Previous experience in fintech, iGaming, or other high-throughput, real-time transactional environments.
   

Why Us:

We provide 20 days of vacation leave per calendar year (plus official national holidays of a country you are based in).

We provide full accounting and legal support in all countries we operate.

We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.

We are looking for Data Engineer in Kraków, Poland who will own the company data lake — the system of record for millions of financial events a day (bets, wallet movements, live odds) across 12M+ active users. You decide how that data lands, is stored, retained, and governed on S3 + Snowflake/Databricks, so analytics, finance, and regulators all see accurate, reconciled data with zero drift from source.

Domain: Regulated iGaming / wallet & ledger data. Audit-heavy: regulators, finance and analytics all consume the same tables. Millions of financial events per day, terabyte-plus scale.
 

What you'll be doing:

• Own the lakehouse architecture: bronze/silver/gold layers, Iceberg/Delta tables, schema evolution.
• Land operational data via CDC streaming (Kafka, Debezium), handling late and duplicate events.
• Design data layout for speed and cost: partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Own retention and archival: storage tiering, regulatory retention, immutability, GDPR deletion.
• Guarantee correctness: freshness SLAs, drift detection, reconciliation against the source wallet and ledger systems.
• Own governance: catalog and lineage, row/column access control, PII masking, encryption, audit trails.
• Monitor ingestion health, data anomalies, and cloud storage/compute spend.
  
  Must-have:
• 3 years' experience in hands-on delivery within that architecture — pipelines, ingestion, models, monitoring
• Lakehouse architecture — bronze/silver/gold layering, an open table format (Iceberg, Delta, or Hudi), schema evolution.
• Data layout & query optimization at TB+ scale — partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Cloud lakehouse/DWH in production — Snowflake, Databricks, or BigQuery.
• CDC & streaming ingestion — Kafka + Debezium or equivalent; late, duplicate and out-of-order events.
• Strong SQL and data modeling — enough relational grounding to reason about the OLTP systems you capture from. Critical for financial ledgers.
• Correctness — freshness SLAs, drift detection, reconciliation against source wallet/ledger systems.
• Governance — catalogs, lineage, row/column access control, PII masking, retention, GDPR deletion.
• Cloud object storage — S3 or GCS, plus storage tiering and archival.

• Python and an orchestrator — Airflow or Dagster, as tools.

   

  Location & work model:

• Kraków, Poland. Hybrid — 2 days per week from the office.

Join our AI Lab as a Trainee AI/ML Engineer and build practical experience developing AI-powered applications with Python, Large Language Models, and AI agents. You will learn from experienced engineers through structured mentorship, contribute to internal and client-facing initiatives, and strengthen your skills through research, experimentation, and hands-on development.
 

Required for this role

• Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field
• Basic knowledge of Python
• Understanding of fundamental software engineering and computer science concepts
• Interest in Artificial Intelligence, Machine Learning, and Generative AI
• Strong motivation to learn new technologies, tools, and frameworks
• Good analytical and problem-solving skills
• Ability to collaborate effectively within a team
• Curiosity, initiative, and a growth mindset
   

Your responsibilities

• Learn and contribute to the development of AI-powered applications with guidance from experienced engineers
• Support the implementation of solutions using Large Language Models, AI agents, and related technologies
• Assist with prompt engineering, testing, and evaluation of AI solutions
• Prepare, clean, and analyze data for AI and Machine Learning projects
• Develop and maintain Python scripts and tools that support AI workflows
• Participate in research, experimentation, and prototyping of new AI capabilities
• Collaborate with team members on internal and client-facing AI initiatives
• Follow emerging AI technologies, frameworks, and industry best practices, and share findings with the AI Lab team
   

What you get

Your time off

• 18 paid vacation days and 10 paid sick days annually
• 10 Ukrainian public holidays
• Maternity and paternity leaves
• Marriage and Parenthood Package
• Additional leave for major life events
• Sabbatical leave opportunities
   

Learning & growth

• Sombra University workshops and internal learning programs
• Tech Communities and knowledge sharing sessions
• Language courses and workshops
• Mentorship opportunities
   

Health & well-being

• Sports compensation or health insurance coverage
• Participation in races and marathons
• Corporate doctor (telemedicine)
• Well-being initiatives and workshops
   

And even more

• Company-provided technical equipment
• PE administration and tax support
• Internal referral program
• IT Club loyalty program
• Company events and volunteering initiatives

Job Title:

Senior Data Engineer

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

Our client is a major online gaming operator building a next-generation transactional platform for user account management. This is a greenfield project designed to operate in a highly regulated, high-volume environment and support millions of concurrent users. The platform will process financial transactions in real time and must ensure high availability, sub-second performance, data integrity, security, and full auditability. One of the key engineering challenges is maintaining reliability and performance during extreme traffic spikes and peak events. 

The team is looking for a hands-on Data Engineer to help build and maintain scalable and reliable data infrastructure, develop production-grade data pipelines, and work with large volumes of business and transactional data, with a strong focus on data quality, integrity and performance.

About the Role:

You will be responsible for the design, implementation, and operation of the data lake and lakehouse architecture, with strong ownership of technical and architectural decisions. This includes designing and building reliable data pipelines, working with large-scale business and transactional data, and defining how data is stored, partitioned, processed, and monitored. You will work closely with Data Engineering, DevOps, Data Science, and other stakeholders to ensure data is accurate, reliable, and available for different use cases. You will also contribute to the data migration, drive data quality and pipeline reliability, and help shape the long-term scalability, performance, and maintainability of the data infrastructure.

Key Responsibilities:

• Design, build, and maintain a multi-layer lakehouse (bronze/silver/gold) using open table formats such as Iceberg and Delta, including schema evolution and data organization
• Design and build reliable CDC and streaming ingestion pipelines using Kafka, Debezium, or similar technologies, including handling late and duplicate events
• Define and optimize data layouts in cloud object storage through partitioning, compaction, file sizing, and query performance optimization
• Define and maintain data retention, archival, immutability, and GDPR-style deletion processes
• Ensure data freshness, quality, and consistency, identify data drift, and drive reconciliation with source transactional systems
• Define and implement data governance practices, including metadata, lineage, access controls, PII protection, encryption, and audit trails
• Ensure pipeline and ingestion reliability, establish effective monitoring, and drive investigation and resolution of data anomalies
• Collaborate with Data Engineering, DevOps, Data Science, Analytics, Finance, and Compliance teams on data requirements, migration, architecture, and long-term technical decisions

Required Competence and Skills:

• 5+ years of data engineering experience working with large-scale data systems, data lakes, or lakehouse environments
• Strong production experience with cloud object storage (AWS S3 or equivalent) and at least one analytics platform such as Snowflake, Databricks, or BigQuery
• Strong hands-on experience with open table formats such as Iceberg, Delta, or Hudi, including schema evolution and data organization
• Strong understanding of partitioning, file layout, data storage, and query optimization for large datasets
• Experience designing and building CDC and streaming data pipelines using Kafka, Debezium, or similar technologies
• Strong SQL and data modeling skills, with solid knowledge of relational databases and transactional data
• Experience with data quality, monitoring, and governance practices, including metadata, lineage, access control, PII handling, and retention
• Hands-on experience with Python for data engineering tasks and experience with workflow orchestration tools such as Airflow or Dagster
• Ability to take ownership of technical decisions, contribute to architecture and system design, and clearly explain technical choices and trade-offs

Nice to Have:

• Experience working with financial, transactional, or ledger data
• Experience in regulated or audit-heavy environments such as fintech, gaming, banking, payments, or similar industries
• Experience participating in large-scale data migration projects

Why Us:

We provide 20 days of vacation leave per calendar year (plus official national holidays of a country you are based in).

We provide full accounting and legal support in all countries we operate.

We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.

We are looking for a Lead Platform Engineer to lead the team behind the Equals 5 web platform, the Laravel + Vue application that pharma marketing teams use to run and measure their campaigns. This is a technical management role: you lead a small team of developers and a QA engineer, and you keep writing code yourself. You propose how a feature should be built, write it down as an FRD, agree it with the CTO, and then take it through review, release and whatever it does in production. You report to the CTO and work day to day with our product lead, who decides what we build and why.

Requirements

General

• 2+ years leading engineers as a tech lead, team lead or hands-on engineering manager: task breakdown, code review, mentoring, 1:1s
• 5+ years building and shipping web products as a full-stack engineer
• Experience carrying a whole product in production: releases, incidents, data correctness
• Comfortable in a fully remote team with hours overlapping EU timezones
• English B2 or above. Most of our communication is written and async

Technical

• Laravel in depth: Eloquent, queues, events, service providers, testing. We run Laravel 12 on PHP 8.3, with queues on Redis
• Vue: the SPA is Vue 2.7 with the Options API, Vuex and Vue Router, and there is no Composition API anywhere in it. Either Vue 2 or Vue 3 experience counts, as long as you can work inside an existing codebase and improve it. The rest of the frontend is dated too (Vite 2, bootstrap-vue 2, chart.js 2, vee-validate 2), so this is a frontend platform question rather than a framework upgrade. What we do about it, and when, is one of the first calls we will ask you to make with us
• PostgreSQL beyond CRUD: indexing, query plans, migrations against live tables. Production is Cloud SQL 14 while local and CI run 17, so a major-version upgrade is on the table
• Real SQL ability plus hands-on work with an analytical store: ClickHouse, BigQuery, Snowflake or similar. Our largest tables run to 1.5 billion rows, and Apache Druid is still in the hot path behind several controllers, so there are three analytical stores to hold in your head
• Writing FRDs and keeping technical documentation current: requirements, architecture decisions, API contracts, data models. We do not have this discipline today, and establishing it is a core part of the role rather than a bonus
• GCP at the service-design level: Cloud Run, Cloud SQL, GCS. You answer for what you deploy
• Docker and CI. GitHub Actions is primary, on a mix of self-hosted and GitHub-hosted runners, and a legacy Bamboo pipeline survives as a manual fallback for production deploys
• Automated testing as a habit. A mature Cypress E2E suite of 135 specs gates every pull request; PHPUnit coverage across 819 PHP files and 206 migrations is uneven, and there are no frontend unit tests at all. Closing that gap is part of the job
• Production debugging with Sentry and Laravel and nginx logs. Observability is thin today and improving it is yours to drive

Soft

• You lead in practice: you split work for other people, review their code, unblock them, and say no when that is the right answer
• You write clearly in English: FRDs, decision records, review comments
• Analytical: you find the root cause before you propose a fix
• Task management: your board reflects what is actually happening, and you raise risks while there is still time to act on them
• Care with healthcare-professional data: access, consent, PII
   

AI baseline:

AI-assisted development is how we ship, not something we are piloting. Concretely, what we expect from day one:

• Daily use of AI coding agents (Claude Code, Cursor, Codex or similar): required, not “I tried it once”
• 50%+ of the code you ship is drafted by agents and reviewed by you line by line
• Able to run 2–4 agents in parallel on isolated branches or worktrees and merge the results yourself
• You maintain agent context for your projects: CLAUDE.md / AGENTS.md, custom commands, rules files. At least 1 such artifact you wrote yourself and keep current
• You automate at least 1 recurring routine per month with an agent: reports, migrations, release checks, test generation
• You can tell when the agent is wrong and throw the output away. Whatever ships, you are the engineer on the hook for it — we ask about this in the interview
   

Nice to have:

• Ad-tech or marketing analytics background: impressions, clicks, attribution windows, audience matching
• Healthcare or pharma domain, especially consent and PII handling
• Infrastructure as code with Pulumi or Terraform. Our infrastructure is in Pulumi, in a separate repository
• Experience migrating a Vue 2 codebase to Vue 3
• TypeScript. There is none in the frontend today. Whether we introduce it, where we start, and whether the cost is worth paying is a call for this role to make
• Apache Druid
• BigQuery and ClickHouse cost and performance tuning
   

Responsibilities:

• Lead a small team of 2–4 developers plus QA: task breakdown, code review, mentoring, input into hiring
• Stay hands-on: you write code yourself as well as distributing it
• Design how features are built (data model, API, UI, tests, rollout), write the FRD, and take it through review with the CTO before the work starts
• Keep technical documentation current after the feature ships: architecture, API contracts, data models
• Work directly with the product lead on weekly priorities
• Query and shape campaign data across PostgreSQL, BigQuery, ClickHouse and Druid, and keep dashboards fast
• Own releases and reliability: deploys across four production VMs with an NFS-shared release directory and automatic rollback, monitoring, alerting, incident response
• Look after the per-PR preview environments, where every pull request gets its own database clone and a routed environment
• Work with healthcare-professional data under our access, consent and PII rules
• Report to the CTO and agree architecture and technical standards with them
   

Tech stack:

PHP 8.3 / Laravel 12 · Vue 2.7, Vuex, Vite · PostgreSQL 14 in production (17 locally and in CI), Redis · ClickHouse, BigQuery, Apache Druid · GCP, Docker, GitHub Actions · Sentry · PHPUnit, Cypress · Claude Code

What we offer:
- 18 days PTO
- 5 days sick leave
- 11 US national holidays
- 50% of course price (if relevant to job)
- Laptop
 

We are looking for an experienced Platforms/Infrastructure Engineer to join a global technology environment and take responsibility for the design, operation, security, and continuous improvement of Microsoft Azure and hybrid infrastructure platforms. The role combines strong Microsoft Azure, Entra ID, Windows/Linux Server, Infrastructure as Code, automation, security, observability, and platform operations expertise. The engineer will help design and maintain secure Azure landing zones, support global infrastructure services and business-critical environments, and act as an escalation point for complex platform issues.

Key Responsibilities:

• Design, build, maintain, and optimise Microsoft Azure infrastructure and hybrid cloud environments.
• Design and maintain secure Azure landing zones, platform guardrails, policies, subscription structures, naming standards, and access controls.
•  Manage and improve Microsoft Entra ID services, including RBAC, PIM, Conditional Access, MFA, SSO, SCIM, administrative units, and identity governance.
• Build, harden, patch, maintain, and decommission Windows Server and Linux workloads across Azure, VMware/ESXi, Azure Local/HCI, colocation, and on-premises environments.
• Support patching and lifecycle management, including virtual patching approaches for legacy operating systems where standard patching is not possible.
• Design and deploy infrastructure using Infrastructure as Code, primarily Terraform and Bicep, as well as ARMtemplates.
• Develop and maintain infrastructure automation using PowerShell, Azure Automation Runbooks, JSON, andYAML.
• Support Azure Automation Runbooks across cloud and hybrid environments, including Hybrid Worker configurations.
• Monitor platform health, capacity, availability, and performance using Azure Monitor, Log Analytics, Azure Managed Grafana, and related observability solutions.
• Ensure platform security and compliance through security hardening, CIS baselines, vulnerability remediation, identity controls, and certificate lifecycle management.
• Manage patching, upgrades, and infrastructure lifecycle activities using Azure Update Manager and automation.
• Participate in incident and problem management, including troubleshooting, root cause analysis, and implementation of long-term solutions.
• Act as an escalation point and subject-matter expert for Azure, Entra ID, and server infrastructure.
• Support highly available and resilient platforms using SRE principles, including HA, load balancing, resilience engineering, and continuous improvement.
• Participate in an on-call rotation and occasional out-of-hours support for critical global services.

Must-Have Technical Skills:

• Microsoft Azure
• Microsoft Entra ID / Azure AD
• Conditional Access, MFA, PIM, RBAC, SSO, SCIM, and identity governance
• Windows Server 2016-2025
• Linux - RHEL / Ubuntu
• Terraform and Bicep
• ARM templates
• PowerShell
• Azure Automation Runbooks and Hybrid Worker environments
• Azure governance, secure landing zones, and platform security
• Azure Monitor, Log Analytics, and Azure Managed Grafana
• Server and cloud security hardening
• Patch and lifecycle management, including legacy OS / virtual patching scenarios
• Hybrid cloud / on-premises infrastructure

Experience Requirements:

• 5+ years of professional IT experience.
• 3+ years of hands-on cloud and infrastructure engineering experience, particularly with Microsoft Azure and hybrid environments.
•  Proven experience supporting large and complex enterprise environments.
• Experience working across distributed/global teams.
• Experience with ITIL-aligned operations, including Incident, Problem, Change, Request Fulfilment, and Service Transition.
• Strong troubleshooting and root cause analysis capabilities.
• Ability to work independently in a remote environment and manage priorities with minimal supervision.
• Strong written and verbal English communication skills.
• Experience in financial services, insurance, or a similarly complex enterprise environment is preferred.

• Relevant Microsoft Azure certification(s) are required.

   

Nice to Have:

• Azure networking: VNets, NSGs, firewalls, private endpoints, and routing.
• VMware / ESXi and Azure Local / HCI.
• Azure DevOps and GitHub Enterprise.CI/CD pipelines for infrastructure deployment.
• Jira and Confluence.
• Experience with cloud cost optimisation, forecasting, and consumption management.

Join Acropolium - Where People Matter, and Ideas Grow!

Acropolium is an international software development and tech consulting company with over 22 years of experience in delivering scalable, human-centric digital solutions. We partner with clients across 20+ industries - from bold startups to established unicorns - helping them grow, adapt, and thrive.
With ISO 9001 certification and 450+ successful applications behind us, we combine deep technical expertise with a people-first culture. Our core team is in it for the long run - over 60% have been with us for more than 10 years - and we’re growing fast.

About the Role
We are looking for a Senior Data Engineer to join a long-term project.
You’ll work on designing and developing scalable data solutions using modern Databricks and Azure technologies, combining hands-on engineering with architectural responsibilities.

Key Responsibilities

• Design and evolve scalable data architectures based on Data Lakehouse principles.
• Define technical solutions and architectural approaches for data processing, integration, and storage.
• Design, develop, and optimize data pipelines using Databricks, Apache Spark, and Python.
• Build and maintain data workflows and integrations using Azure Data Factory and Azure Storage.
• Establish and maintain data governance, access control, and data management practices using Unity Catalog.
• Ensure data solutions are scalable, reliable, secure, and maintainable.
• Collaborate with engineers and other stakeholders to translate business and technical requirements into effective data solutions.
• Contribute to technical decisions, architecture documentation, and best practices across the project.
• Identify opportunities to improve existing data solutions, processes, and overall platform performance.
   

What We’re Looking For

• Senior-level experience in Data Engineering, with strong architectural thinking
• Hands-on experience with Databricks, Spark, and Python
• Solid understanding of Data Lakehouse architecture
• Practical experience with Unity Catalog, Azure Data Factory, and Azure Storage
• Ability to design scalable and reliable data solutions and make technical decisions
• English - B2 or higher
• German B1 will be a plus
   

Why join us?

• A culture built on empathy, transparency, and long-term relationships
• Average tenure of 5+ years — we’re a place where people stay
• Personal development plans, structured career growth, and regular performance reviews
• Strong internal communication and supportive onboarding
• Learning budget & access to internal training
• Flexibility to be yourself — we encourage individuality and family values
• Support for employees in the military
• Clients who return with new ideas — and trust us to deliver
   

Let’s DARE TO CREATE together.
Join Acropolium - not just a job, but a place to belong.

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for a Senior Data Engineer to join our growing Data & Analytics practice and build modern data solutions for our customers on AWS.

This is a highly hands-on engineering role. You’ll work across different customer environments and data challenges, from modernizing legacy pipelines and building lakehouse platforms to optimizing analytical workloads and owning smaller data platforms end-to-end.

You won’t be limited to one stack or one long-running product. One project may involve Spark and Apache Iceberg, another Redshift performance and data modeling, and another Kafka, OpenSearch, or a new data platform built from ingestion through analytics.

We’re looking for someone who can take ownership of a data engineering problem, make sound technical decisions, and deliver independently without needing constant direction.

📍 Work location: Ukraine, remote

If you are interested in this opportunity, please submit your CV in English.

Responsibilities

• Design, build, and optimize production data pipelines and data platforms on AWS.
• Build scalable data processing workloads using Spark/PySpark, Python, and SQL.
• Build and modernize data lakes and lakehouse architectures, working with technologies such as Amazon S3, Apache Iceberg, AWS Glue, and related data services.
• Design, troubleshoot, and optimize analytical workloads and data warehouses, including Amazon Redshift and other OLAP platforms.
• Modernize legacy and inefficient data workloads, including moving existing ETL/ELT processing to scalable distributed data architectures.
• Work with streaming and event-driven data pipelines using technologies such as Kafka, Kinesis Data Firehose, and related services.
• Investigate real production data problems, from performance bottlenecks and data modeling issues to upstream/downstream dependencies, and recommend practical solutions.
• Take ownership of customer projects end-to-end, from understanding the technical problem through implementation, testing, and delivery.
• Work directly with customers when needed to clarify requirements, explain technical decisions, and recommend solutions based on your expertise.
• Build data foundations for analytics and ML workloads, collaborating with Data Scientists, DevOps, and MLOps engineers.
• Contribute to dashboards and analytics when required using QuickSight or other BI tools.

• Apply practical engineering practices around data quality, testing, CI/CD, security, governance, and infrastructure automation.

   

Requirements

• Strong production experience in Data Engineering, with the ability to independently own and deliver data projects.
• Deep hands-on experience with Apache Spark / PySpark and distributed data processing.
• Strong Python and SQL skills.
• Hands-on experience building data solutions on AWS.
• Strong understanding of data lakes, lakehouse architectures, ETL/ELT, data modeling, and data processing at scale.
• Experience with analytical databases and data warehouses. Strong Amazon Redshift experience is highly valuable; deep experience with platforms such as Snowflake, Synapse, or similar OLAP technologies is also relevant.
• Understanding of streaming architectures and technologies such as Kafka or Amazon Kinesis.
• Ability to troubleshoot existing data systems, understand how data flows across upstream and downstream components, and improve performance and reliability.
• Ability to work autonomously, make pragmatic technical decisions, and take responsibility for delivery.

• Good communication skills and the ability to discuss technical problems and solutions directly with customers in English.

   

Nice to have

• Experience with Apache Iceberg or other modern open table formats and lakehouse technologies.
• Amazon OpenSearch Service / Elasticsearch experience.
• Experience with BI and visualization tools such as Amazon QuickSight, Tableau, or Power BI.
• Terraform and Infrastructure as Code.
• CI/CD practices for data pipelines, including data testing and validation.
• Experience preparing data platforms for ML workloads using services such as Amazon SageMaker.
• Exposure to Amazon Bedrock, GenAI, or agentic development.

• Experience with Databricks, Snowflake, ClickHouse, or other modern data platforms

   

What Success Looks Like in Year One

• Independently owns assigned data projects end-to-end, from understanding the problem and making technical decisions to implementation, troubleshooting, testing and successful delivery, with minimal supervision.
• Has built strong practical expertise across the team’s core data stack and can confidently work across different customer environments, including Spark, Python/SQL, data lakes/lakehouses, analytical and NoSQL databases, streaming and other technologies used by the team.
• Can investigate unfamiliar data problems independently, identify performance or data-modeling issues, understand upstream/downstream dependencies, and recommend pragmatic solutions to customers.

• Continuously expands technical depth, keeps up with modern Data technologies and patterns, and can quickly learn new technologies as customer projects and the Data practice evolve.

   

Benefits

• Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
• International work environment
• Referral program — enjoy cooperation with your colleagues and get a bonus
• Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
• English classes

• Soft skills training

   

Country-specific benefits will be discussed during the hiring process.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all and believes that a diverse workforce is crucial to our success. Our recruitment decisions are based on your experience and skills, recognising the value you bring to our

We are looking for a Senior Voice Operations Engineer with strong expertise in Linux infrastructure and VoIP technologies to join a Healthcare product.
 

You will manage and modernize a large distributed environment that supports telephony services, LAMP applications, Asterisk, FreePBX, and enterprise SIP phones.

RESPONSIBILITIES:

• Maintain, secure, and update a distributed Linux infrastructure and its voice services;
• Manage SIP phone provisioning, configurations, firmware, and certificates;
• Support SIP trunks, call routing, failover, caller ID, and carrier integrations;
• Automate fleet management, deployments, and recovery processes;
• Monitor production systems, investigate incidents, and eliminate recurring issues;
• Troubleshoot Asterisk, FreePBX, SIP, RTP, DTMF, NAT, routing, and audio problems.

REQUIREMENTS:

• Senior-level experience with Linux administration in distributed production environments;
• Strong production expertise with Asterisk, FreePBX, SIP, RTP, SDP, and IP telephony;
• Ability to analyze SIP messages, packet captures, dialplans, and end-to-end call flows;
• Solid knowledge of TCP/IP, UDP, DNS, DHCP, NAT, firewalls, VLANs, TLS, and Linux networking;
• Experience managing SIP phones, provisioning, firmware, carrier trunks, and number routing;
• Strong scripting skills in Bash, Python, or a similar language;
• Experience with Git, configuration management, monitoring, and incident response.

WILL BE PLUS:

• Experience with Yealink or similar enterprise SIP phones;
• Knowledge of Kamailio, OpenSIPS, FreeSWITCH, or session border controllers;
• Experience with Puppet or other fleet-management tools;
• Experience modernizing legacy Linux, LAMP, Asterisk, or FreePBX environments;
• Background in Healthcare or another security-sensitive domain.

MAIN BENEFITS:

• Flexible working hours;
• 25 paid days off and 10 sick/medical leaves;
• Additional paid days off for personal events;
• Maternity/Paternity leaves;
• Medical insurance;
• Self-development budget;
• Referral bonuses;
• Corporate events and gifts;
• Speaking Clubs;
• PE accounting and support.

Cloudfresh ⛅️ is a Global Google Cloud Premier Partner, Google Chrome Enterprise Partner, Zendesk Advanced Partner, Asana Platinum Solutions Partner, GitLab Select Partner, HubSpot Diamond Partner, Miro Solution Partner, Okta Activate Partner, Cloudflare Powered+ Partner, JumpCloud Platinum Partner, and Microsoft Partner.

We’re on the mission to #GenerateTheReal✨ for people and organizations. Real growth happens when technology truly supports people — not the other way around. That’s why we focus on empowering teams with leading cloud ecosystems and certified experts, helping companies move faster across AI, Data, Cybersecurity, and beyond.

As a FinOps Data Engineer at Cloudfresh, you will play a key role in owning and industrializing Cloudfresh’s financial data infrastructure, building a BigQuery-based platform that unifies billing, project, bank, and operational data into a single source of truth across our global entities.

Requirements:

• Deep practical knowledge of the GCP native data stack (BigQuery, Cloud Storage, Cloud Functions, BigQuery ML).
• Strong experience with data integration and orchestration tools (Dataflow, Dataproc, Pub/Sub, Cloud Composer, or Dataform) for building optimized ETL/ELT pipelines.
• Proficiency with analytics and visualization tools, specifically Looker or Looker Studio.
• Solid understanding of FinOps processes, unit economics, cloud pricing models, and financial metrics (e.g., MRR/ARR, marginality).
• Experience integrating data from APIs, relational databases, and financial/billing systems.
• Strong data engineering background with data warehousing and data modeling capabilities.
• High autonomy, extreme ownership, and a proactive, self-driven mindset.
• Fast and accurate execution paired with strong analytical and problem-solving skills.
• Excellent communication skills with the ability to explain complex technical concepts simply to non-technical stakeholders.

Responsibilities:

• Build and maintain automated BigQuery ETL/ELT data pipelines ingesting data from billing systems, Asana, HubSpot CRM, bank feeds and vendor data across global entities.
• Automate multi-entity invoice consolidation and marginality reporting (by entity, customer, vendor) to replace manual spreadsheet models.
• Design and implement SaaS metrics data models (MRR/ARR and others) and OPEX/Professional Services cost allocation pipelines.
• Develop automated monthly management reporting, double-entry architecture reconciliations (cash vs. accrual basis), and consolidated financial statement data pipelines.
• Create interactive Looker Studio dashboards and self-service reporting environments for key stakeholders.
• Implement built-in data quality controls, automated checks, and AI data processing/validation for financial integrity and unit economics alerts.
• Document data lineage, audit data sources, and maintain robust forward-looking capabilities such as anomaly detection and budget-vs-actual tracking.

Nice to have

• FinOps Foundation Certified Practitioner (FOCP) or equivalent certification.
• Experience working in consulting or client-facing environments.
• Experience with infrastructure-as-code tools (Terraform, Deployment Manager, or similar).
• Familiarity with Kubernetes cost optimization and containerized workloads.
• Experience integrating cost optimization into CI/CD or DevOps workflows.

Work conditions

• Competitive Salary & Transparent Motivation: Receive a competitive base salary with performance-based bonuses, providing clear financial rewards for your success.
• Flexible Work Format: Work remotely with flexible hours, allowing you to balance your professional and personal life efficiently.
• Training with Leading Cloud Products: Access in-depth training on cutting-edge cloud 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. 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.

About the role
We're looking for a hands-on data platform expert to own the reliability, performance, and cost-efficiency of our modern data stack, built on Snowflake, dbt, and managed ELT. You'll be the go-to person for how data flows, how fast it runs, and how much it costs. You'll also shape the operational standards our data teams build on.

If you're a strong Data Architect or Senior Data Engineer who enjoys making platforms run smoothly at scale, we'd love to talk.

What you'll do

• Own Snowflake performance and cost. Tune queries, right-size warehouses, track credit consumption, and drive cost optimisation and budget planning.
• Keep dbt production-grade. Maintain models, automated tests, documentation, and deployment processes.
• Run reliable ELT. Oversee pipelines end to end so data stays available, fresh, and within SLA.
• Build observability. Set up monitoring, alerting, and incident management, and lead root cause analysis and prevention.
• Set the data quality bar. Define standards, automate validation, and embed observability across the platform.
• Govern access and compliance. Manage access controls, governance policies, and regulatory requirements.
• Scale good practices. Create runbooks, CI/CD workflows, and engineering standards for data teams.
• Partner across functions. Work with Data Engineering, Analytics, BI, Security, and Finance to raise platform reliability and efficiency.

What you bring

• Extensive experience in Data Engineering, Analytics Engineering, DataOps, or Data Platform Operations
• Strong hands-on Snowflake expertise: administration, performance tuning, warehouse management, and SQL optimisation
• Advanced dbt (Core or Cloud) skills, including incremental models, macros, testing, and deployment
• Proven experience running production ELT with Fivetran, Airbyte, Stitch, or similar tools
• Solid grasp of DataOps practices: CI/CD, version control, automated testing, and deployment pipelines
• Track record of implementing data quality frameworks, monitoring, alerting, and incident response
• Understanding of data governance, access management, and platform reliability

Nice to have

• Data observability tools such as Monte Carlo, Elementary, or Datafold
• Orchestration with Airflow, Dagster, or Prefect
• Hands-on experience cutting cloud data platform costs, especially Snowflake credits
• Familiarity with GDPR and SOC 2

REMOTE

Підтримка та розвиток Kubernetes-платформи: CI/CD, бази даних, мережа, моніторинг, бекапи та DR.

Стек:

• Orchestration: Kubernetes (k3s), Helm, KEDA

• CI/CD: GitLab CI

• Databases: PostgreSQL (CloudNativePG, PgBouncer)

• Cache & queues: Valkey / Redis, RabbitMQ

• Storage & backup: MinIO (S3), Velero

• Network & edge: ingress-nginx, Cloudflare (DNS, Tunnel, TLS, CDN), WireGuard

• IaC & secrets: Terraform, Ansible, SealedSecrets

• Observability: Prometheus, Alertmanager, Grafana, Loki, Alloy

Вимоги:

• Більше року досвіду в DevOps / SRE, досвід роботи з Kubernetes у продакшені, включно із self-managed кластерами (k3s, kubeadm тощо)

• PostgreSQL у Kubernetes: реплікація, бекапи, відновлення, connection pooling

• Linux та мережі: файрволи, DNS, TLS, VPN

• Terraform, Ansible

• Метрики, алерти та логи на стеку Prometheus / Grafana / Loki

• Практичний досвід disaster recovery

Буде плюсом:

CloudNativePG, KEDA, Velero, Cloudflare Zero Trust, GitOps (Argo CD / Flux).

Пропонуємо:

• віддалений формат роботи

• графік, який обговорюється

• оплату чергувань

• оплачувану відпустку

• оплачувані лікарняніch / Grafana)
 

Domain: Banking & Finance
Experience: 5+ years
Employment Type: Full-time
Location: Hybrid — Kraków, Poland
Project Duration: November 2, 2026 – April 30, 2027

About the Project

We are looking for Data Engineer to join a modern engineering environment and work on reliable, scalable data solutions.

The role focuses on building data pipelines and processing frameworks, integrating multiple platforms and systems, and making data accessible, governed, and fit for purpose. The team works with Scala, Java, Python, Apache Airflow, and cloud technologies.

Experience with data fabrics, cloud platforms, and Apache Airflow will be particularly valuable.

Responsibilities

• Design, develop, and maintain robust data pipelines and engineering frameworks
• Build scalable data processing solutions using Scala, Java, and Python
• Integrate data platforms, applications, APIs, and other technology components
• Develop and support workflows using Apache Airflow
• Work with cloud-based data services and infrastructure
• Contribute to data fabric architectures, including data integration, discovery, access, and governance
• Develop and consume APIs to support data movement and system interoperability
• Apply software engineering best practices, including version control, testing, code reviews, documentation, and CI/CD
• Monitor, troubleshoot, and improve the performance, resilience, and quality of data solutions
• Collaborate with data architects, software engineers, platform teams, analysts, and business stakeholders
• Support security, data protection, and operational controls throughout the engineering lifecycle

Requirements

• Professional experience in data engineering, software engineering, or a closely related field
• Strong programming skills in Scala, Java, and Python
• Strong knowledge of SQL / DML
• Experience designing and implementing data pipelines, processing frameworks, or integration services
• Experience integrating multiple tools, platforms, and systems
• Hands-on experience with Apache Airflow, including DAG development, scheduling, monitoring, and troubleshooting
• Understanding of REST APIs, authentication, and error handling
• Experience working with cloud platforms and cloud-native engineering practices
• Familiarity with data architecture, data integration patterns, and data quality
• Experience with Git, automated testing, and CI/CD
• Ability to investigate complex technical issues and communicate solutions clearly
• Degree, equivalent qualification, or demonstrable professional experience in computer science, engineering, data, mathematics, or a related field

Nice to Have

• Experience with Google Cloud Platform (GCP), including BigQuery, Cloud Storage, Pub/Sub, Dataflow, or Dataproc
• Experience with data fabric architectures, metadata, data catalogues, lineage, or federated data access
• Familiarity with Apache Spark and other distributed processing technologies
• Experience with containers, orchestration, or Infrastructure as Code
• Knowledge of security, access control, encryption, and regulatory requirements in data environments
• Experience working in Agile, product-oriented, or DevOps environments
• Relevant cloud, data engineering, or software engineering certifications

Soft Skills

• Strong ownership and accountability for deliverables
• Ability to collaborate effectively across engineering, architecture, operations, and business teams
• Ability to balance delivery speed with maintainability, resilience, and control
• Structured approach to problem-solving and continuous improvement
• Ability to communicate technical concepts clearly to both technical and non-technical stakeholders
• Collaborative mindset and appreciation of diverse perspectives

Tech Stack

Scala, Python, Java, SQL, Apache Airflow, Apache Spark, REST APIs, Git, CI/CD, GCP, Data Integration, Data Fabric

We are looking for a Computer Vision Engineer to join one of our international clients on an outstaff basis.

🎯 Core Responsibilities

• Video Algorithmic Pipelines: Design and stitch together custom video processing workflows (Detection ➔ Multi-Object Tracking ➔ Action Recognition) directly in memory (GPU/CUDA level) to minimize latency.
• Camera Calibration & Homography: Develop robust algorithms for automatic pitch/field line detection, lens distortion compensation, and 2D-to-3D coordinate mapping (homography estimation).
• Advanced Multi-Object Tracking (MOT): Optimize tracking algorithms (Kalman filters, deep embedding matching) to handle dense crowds, severe occlusions, and ID switches under fast-paced sports dynamics.
• Custom Model Adaptation: Fine-tune and structurally modify state-of-the-art CV architectures (YOLO-style detectors, Vision Transformers) specifically for sports domains and low-resolution/far-angle edge cases.

🛠 Technical Requirements

• CV Experience: 2+ years of production-proven commercial experience in Computer Vision, with a heavy focus on Video Analytics.
• Domain Expertise: Solid, demonstrable experience with sports video data, player tracking, or highly dynamic multi-agent scenes.
• Hard Skills:
  • Exceptional Python and deep understanding of PyTorch or TensorFlow.
  • Strong foundation in classical computer vision geometry (projective geometry, epipolar geometry, camera matrices, OpenCV).
  • Hands-on experience with modern tracking frameworks (e.g., ByteTrack, OC-SORT) or writing proprietary tracking logic.
  • Experience optimizing models for inference (TensorRT, ONNX, Triton Inference Server).
• Engineering Mindset: Ability to profile vision algorithms, identify execution bottlenecks, and optimize for throughput/latency without losing precision.

Project Highlights:

• Duration: Long-term stability (3+ years).
• Work Setup: 100% Remote.
• Engagement Model: Project-based employment / contractual setup (specific employment model and local arrangements to be discussed).

Key Requirements:

• Core Tech: Strong hands-on expertise with Python and PySpark.
• Cloud & Data Stack: Proven experience with Azure Databricks and Azure Data Lake (ADLS).
• Data Engineering: Deep background in designing ETL/ELT processes, building Data Lakehouse architectures, and optimizing data pipelines.
• Languages: Fluent English (Upper-Intermediate or higher).

Nice to Have:

• Experience with Azure Data Factory and Unity Catalog.
•  

What We Offer:

• Work on complex, high-impact engineering challenges in Big Data.
• Long-term stability without the hassle of jumping between short gig assignments.
• Fully remote flexibility.