Jobs Data Engineer

We are looking for a Mid 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.

ZentixSoft is looking for a Data Architect! 🚀

Format: Full-Time Outstaffing, long-term contract (12 months), 100% Remote.

💡 Why ZentixSoft?
 💸 Salary Review: A structured system of compensation reviews aligned with your engineering growth. 
⚖️ Work-Life Balance: Sustainable workflows with no senseless overtime, helping you keep your resource fully charged. 
🦾 Trust and Transparency: Zero micromanagement and no invasive time-trackers—we fully trust your professional autonomy. 
🎁 Gifts & Culture: Corporate gifts for holidays, birthdays, and a true team atmosphere where your voice always matters.
 

🧩 Role Overview & Responsibilities:

We are looking for a hands-on Data Architect to take full architecture ownership of a complex data platform. In this role, you will assess, stabilize, improve, and modernize the existing architecture, delivering rapid, actionable engineering recommendations.

• Architecture Ownership: Define tactical (immediate stabilization) and target (long-term modernization) architectures that engineering teams can directly execute.
• Platform Modernization: Assess complex, legacy data environments; identify performance/scalability bottlenecks; and deliver clear, implementation-ready architectural outputs.
• Unstructured & Structured Data: Architect data pipelines and modeling frameworks capable of handling unstructured data (documents, PDFs, free text, scanned content, images) and structured/file-based datasets.
• Tech-Neutral Evaluation: Evaluate enterprise technologies based on scale, cost, workload, and business requirements rather than preference (determining when Databricks, Synapse, ADF, or Azure SQL are truly justified).
• Data Modeling & Pipelines: Design robust ETL/ELT frameworks, dimensional schemas, star schemas, and incremental processing/refresh strategies.
• Documentation & Governance: Create detailed architecture diagrams, technical decision logs, and data governance standards.
   

🎓 Our Perfect Match (Requirements):

• Proven Architecture Ownership: 4+ years of recent, demonstrated experience as a Data Architect owning overall Azure Data Platform architectures (candidate must be an Architect, not solely a Data Engineer).
• Azure Data Platform Expertise: Extensive hands-on background with Azure Data Services, including Azure SQL / SQL Server, Azure Data Lake, Azure Data Factory (ADF), Azure Synapse, and Databricks.
• Azure Cosmos DB (MUST-HAVE): Hands-on experience working with Azure Cosmos DB and file-based data stores within enterprise environments.
• Data Engineering Core: Strong mastery of Python, SQL, ETL/ELT, and Lakehouse architectures.
• Unstructured Data Experience: Proven track record handling unstructured data sources (documents, PDFs, text, scanned files).
• Fast Delivery & Problem Solving: Ability to onboard quickly into complex environments, navigate incomplete documentation, and deliver meaningful architectural guidance rapidly.
• Communication & English: Fluent English (C1) with exceptional technical writing and stakeholder communication skills.
   

📍 Project Details:

⏳ Duration: 12 Months (Full-time, 100% Remote).

🕒 Time Zone: CET / CEST.
 

We look forward to receiving your CV (please ensure your CV details your project contexts, architecture ownership, and hands-on Cosmos DB/Azure experience).

We’re looking for a Senior Data Engineer (7+ years of experience) to join a project for a client in the energy industry.

What you’ll do

• Design, develop, and improve data pipelines within an Azure Data Platform
• Integrate new data sources into existing Data Lake and analytics environments
• Work with Azure Databricks and build scalable data solutions
• Develop and improve KPI and reporting solutions
• Consolidate and transform data from multiple sources
• Contribute to data modeling, data preparation, and optimization of existing data structures
• Support the development of new KPI dashboards
• Build and deploy solutions following CI/CD practices
• Collaborate closely with Data Analysts, Business Stakeholders, and Product Owners

What we’re looking for

• 7+ years of experience in Data Engineering
• Strong hands-on experience with Microsoft Azure
• Strong expertise in Databricks / Azure Databricks
• Experience building and operating Data Lakes
• Good understanding of CI/CD for data environments
• Experience working with enterprise-scale data platforms
• Strong English communication skills
• Independent, pragmatic, and result-oriented approach

Nice to have

• Experience with AWS data services
• Background in KPI, reporting, or analytics projects
• Experience in the energy or utilities sector

About us

Prizmar is a young professional services and intelligence company that is turning complex financial flows and trading operations into automated, high-integrity pipelines. We operate in Fintech and Agri streams. 
Prizmar Fintech has two verticals – Treasury and Payments. Prizmar Fintech helps CFOs, treasurers and payments teams at mid-to-large corporates, PSPs and fintechs in France and Europe architect their treasury and payments operations. Prizmar is focused on a data-driven and tech-driven approach to the management of treasury and payments operations.
 

The role

You will work directly with the co-founder of Prizmar, who has extensive experience running treasury, payments and bank connectivity. Your mission: build the automation and AI tooling that powers our client engagements. You will write code, structure data and test AI agents on real treasury and payments flows. The domain expertise is learned with us, on the job.

We are looking for someone who treats the business as their own, spots what should be improved and takes it forward.

What you will do

• Own the design and delivery of automation modules in Python and SQL: bank and PSP file parsers (ISO 20022 PAIN/CAMT, MT940, PSP settlement reports and invoices), reconciliation, PSP fee control and cash-forecast pipelines
• Design, build, evaluate and document LLM agents for finance workflows (document extraction, reconciliation, regulatory monitoring) using tool use, retrieval and evaluation sets, with guardrails and human-in-the-loop controls
• Integrate client TMS, ERP and bank connectivity data (Kyriba, SAP S/4HANA, SWIFT, host-to-host) through APIs and file interfaces
• Maintain our shared data foundation: normalized schemas, a growing mapping library, orchestration and CI (Postgres, GitHub Actions or equivalent)
• Take part in client workshops with the founder: map treasury and payment flows, identify automation opportunities, estimate effort and present technical solutions
• Build cost, cash and reconciliation models and dashboards (Excel, Power BI or similar) as client-facing deliverables
• Contribute to client deliverables (diagnostics, target operating models, business cases, vendor selections) under the founder’s supervision
• Guide a junior analyst on code quality and delivery practices as the team grows
  
  What you bring
• A Master’s degree (or equivalent) in engineering, data, computer science, corporate finance or a related field
• 2–4 years of professional experience building and running data or automation solutions in production (finance, payments, banking, fintech, audit or consulting preferred)
• Strong Python (pandas, packaging, testing) and SQL, comfortable with Git, APIs, JSON/XML and CI
• Hands-on experience building with LLMs through APIs: structured outputs, tool use or retrieval, and a method for evaluating reliability
• Working knowledge of payments or treasury data (bank statements, payment files, reconciliation logic) or the ability to acquire it quickly
• Advanced Excel and hands-on Power BI or a similar BI tool
• Daily use of AI tools such as Claude or ChatGPT in your work
• Ownership mindset: you take initiative, look at how the company’s processes work as a whole, and propose improvements without waiting to be asked
• Fluent English
• You work independently, communicate clearly and are eager to shape the company’s growth
  
  Nice to have
• Exposure to ISO 20022 messages (pain.001, camt.053), SEPA or SWIFT
• Exposure to a TMS or ERP (Kyriba, SAP S/4HANA or similar)
• Postgres, dbt, Docker, a cloud platform or Streamlit

• French, German, Spanish or another European language
   

  What we offer

• A competitive fixed base salary
• A significant performance-based annual bonus of up to 40% of base salary, linked to individual objectives, client delivery and Prizmar’s results
• Direct work with founders and tailored training in treasury, payments and AI automation
• Remote work (temporarily hybrid if needed), with flexible hours around core hours of 10:00–16:00 Eastern European time
• A culture where strategic ideas are incentivized and rewarded
• Regular salary review and a wider scope as the company grows
• Power backup for Ukraine-based hires after the trial

• Paid certifications in best-in-class treasury management and payments processing solutions, as well as in AI and data engineering

  
  Hiring process. CV review (a GitHub link or project is a plus), 45–60 minute intro call, practical technical exercise of about 60 minutes on synthetic data, technical interview with both founders. We reply to every applicant within 2 weeks.
   

  Personal data. By applying, you agree that Prizmar processes your CV and personal data for recruitment purposes only, in line with the GDPR. We keep your data for up to 6 months after the process ends and delete it on request.
   

Join the Dataforest Team!

We are actively expanding our team and looking for strong new talent to join our ranks. If you are a Data Engineer seeking a friendly team, a healthy working environment, and a flexible schedule — you have found the right place to send your CV!

You'll be a great fit if you have:

- 2+ years of experience with Python;
- 2+ years of experience as a Data Engineer;
- Experience with Pandas;
- Experience with SQL DB / NoSQL (Redis, Mongo, Elasticsearch) / BigQuery;
- Familiarity with Amazon Web Services;
- Experience with Kafka;
- Knowledge of data algorithms and data structures is a MUST;
- Working with high volume tables 10m+.

What you'll do:

- Create ETL pipelines and data management solutions (API, Integration logic);
- Different data processing algorithms;
- Involvement in creation of forecasting, recommendation, and classification models.

Would be a plus:

- Experience with Spark (pyspark);
- Experience with Airflow;
- Experience in statistics;
- Knowledge of DS and Machine learning algorithms.

We ensure your growth with:

- Great networking opportunities with international clients, challenging tasks;
- Building interesting projects from scratch using new technologies;
- Personal and professional development opportunities;
- Competitive salary fixed in USD;
- Paid vacation and sick leaves;
- Flexible work schedule;
- Friendly working environment with minimal hierarchy;
- Team building activities, corporate events.

Dataforest is looking for a growth-oriented Data Engineer to join our friendly team. As a Data Engineer, you will solve engaging problems using cutting-edge technologies for data collection, processing, analysis, and monitoring.

If you aren't afraid of challenges, this position is just for you!

You'll be a great fit if you have:

- 1+ years of experience as a Data Engineer;
- Strong experience with Python;
- Proven experience with Databricks and Datafactory;
- Experience with AWS/Azure;
- Experience building ETL / ELT pipelines;
- Proficiency in SQL.

What you'll do:

- Developing ETL/ELT pipelines and data management solutions;
- Implementing data processing algorithms;
- Writing and optimizing SQL queries for data extraction and analysis;
- Analyzing data and applying processing algorithms to solve business problems.

We ensure your growth with:

- Working with a high-skilled engineering team on engaging and complex projects;
- Hands-on experience with the latest technologies;
- Direct communication with international clients and challenging tasks;
- Opportunities for personal and professional growth;
- Competitive salary pegged to USD;
- Paid vacation and sick leave;
- Flexible working hours;
- Friendly working atmosphere with zero bureaucracy;
- Plenty of team traditions — corporate parties, team buildings, themed events, and much more!

US Company is searching for a Data & Automation Engineer to join an AI-first product company building an advanced communications platform powered by Large Language Models (LLMs). This is an exciting opportunity to work across the full technology stack, designing scalable backend services, modern user interfaces, cloud infrastructure, and AI-powered features in close collaboration with a US-based engineering team. Full-time, official contract, remote work, and flexible schedule (covering US business hours till 2pm, CST).
 

Brief project description:
The product is an AI-driven communications platform that enables businesses to automate and enhance customer interactions through voice, messaging, and intelligent workflows powered by modern LLM technologies.

Responsibilities:

• Build and maintain data workflows using PostgreSQL and AWS.
• Automate processes with Lambda, EventBridge, and APIs.
• Develop and maintain reports, dashboards, and data integrations.
• Troubleshoot data issues and ensure data quality and reliability..

Required skills (must have):

• Strong PostgreSQL/SQL skills: 5+ years.
• Hands-on AWS experience, especially Lambda and EventBridge.
• Handling Webhooks ~> API Gateway ~> Lambda
• Event/Service Bus ~> Lambda ~> Outgoing API
• Terraform
• Experience with REST APIs and reporting/BI tools.
• Strong analytical, troubleshooting, and communication skills.
• 3+ years in data engineering, analytics, or a related technical role.
• Excellent English and ability to work independently.

Would be considered as a plus:

• Python or Node.js/TypeScript.
• Experience with additional AWS services.
• Experience with BI tools such as Power BI, Tableau, or Metabase.
• Experience with ETL/ELT, dbt, or Infrastructure-as-Code.

Work conditions:

• 100% remote work.
• Full-time (40 hours per week).
• Official contract.
• Flexible schedule with required overlap with US business hours (till 2pm, CST).

Interviewing / Hiring process:
Step 1 - preliminary interview (main questions) - 45 mins
Step 2 - internal technical interview - 60 mins
Step 3 - technical interview with Team Leader - 60 mins

Fundable Senior Data Engineer

Build a data platform that turns information from many sources into reliable, up-to-date company intelligence.

About Fundable

Fundable is a startup data platform that helps investors and other teams discover and research companies. We bring together information on companies, investors, funding activity, and people to make that research easier.

Role overview

We’re looking for a senior data engineer to own Fundable’s data platform from architecture through production, reliably turning high volumes of data from provider APIs, webhooks, files, and web scraping into trusted records for our products.

Today, our enrichment and extraction workflows largely run serially inside a container. You’ll lead the move to a platform that supports concurrent processing, event-driven intake, and orchestrated batch workflows.

What you’ll own

• Multi-source intake: Build concurrent ingestion for provider APIs, webhooks, files, and scraping, with rate limits and failure handling.
• Batch and event-driven execution: Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect. Build event-driven intake with Pub/Sub or Cloud Tasks, including retries, dead-letter queues, and controlled replay of failed work.
• Raw-to-curated data: Preserve original payloads, including provider JSON, in Google Cloud Storage (GCS) before transforming them with dbt. Establish medallion layers for raw, standardized, and curated data.
• Correctness through recovery: Implement idempotent upserts and safe retries, replay, and historical backfills that avoid duplicate records or data corruption. Make data traceable to its source and safe to reprocess.
• Data quality and delivery: Define data contracts, build automated validation and pipeline tests, and monitor freshness and failures. Design and optimize PostgreSQL schemas, queries, and loading patterns so downstream products receive dependable data.
• Extraction and enrichment: Integrate provider enrichment and LLM-based structured extraction from text into the platform.

Must-have experience

• Experience building multi-source production data platforms from zero to one, from initial architecture and pipeline implementation through deployment and ongoing operation.
• Production experience with workflow orchestration tools such as Airflow, Dagster, Prefect, or Temporal, including dependencies, retries, and failure recovery. Airflow experience is preferred.
• Hands-on production experience with dbt and GCP.
• Experience building event-driven systems with messaging or task queues, including asynchronous processing and failure recovery.
• Strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading. Experience with data contracts, raw and curated storage, retention policies, and automated quality checks.
• Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
• Hands-on web scraping experience with tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrent scraping, rate limits, and failure handling.

Nice-to-have experience

• Experience working in startups or small engineering teams, with broad ownership and changing priorities.
• Entity resolution experience is especially valuable: matching company and people records across sources and reconciling duplicates into consistent entities.
• LLM-based structured extraction or company and people data enrichment.
• Familiarity with BigQuery and Dataform.

Location and hours

There is no required location or working-hours overlap.

We are seeking a skilled ML/AI Engineer to join our team and deliver cutting-edge ML/AI models in the ASR domain and legal insights more broadly. The core of this role is building, deploying, and serving ML models in production. 

A key part of this role is speech engineering — prior ASR/speech experience is not a must, but a willingness to learn and grow into this domain is essential. Experience with LLM-based solutions and agentic flows is an advantage, not a requirement. 

What you’ll do: 

• Build, deploy, and serve ML models in production — the core of the role — optimizing inference performance, latency, and efficient GPU utilization. 
• Support and extend our speech solutions, including 3rd-party ASR integrations and self-hosted ML models. 
• Evaluate and optimize key ASR/ML metrics such as WER, latency, F1 score, EER, and DER. 
• Secondary: Integrate LLMs and agentic flows into production solutions where they add value, including fine-tuning existing models. 

What you’ll bring: 

Core (required): 

• Solid background in machine/deep learning, with 4+ years of experience. 
• Strong Python skills and experience working in Linux environments. 
• Familiarity with deep learning libraries (e.g., PyTorch, TensorFlow) and training workflows. 
• Experience with MLOps pipelines and serving/deploying models in production. 
• Experience optimizing models for inference, including GPU acceleration. 
• Experience with Docker, gRPC, and serverless / micro-services architectures on cloud infrastructure (AWS/GCP/Azure). 
• Experience with real-time / streaming systems and low-latency processing (e.g. streaming pipelines, live sessions). 

Nice to have (secondary / optional): 

• Experience building and productizing agentic flows and AI-driven solutions (1+ year). 
• Familiarity with LLM / agent orchestration frameworks such as CrewAI, AWS Bedrock, Temporal, AgentCore, or LiteLLM. 
• Prior ASR / speech experience — not required; willingness to learn this domain is what matters most. 

About Our Client
 

Our client builds and operates GPU cloud infrastructure at the scale modern AI demands. They run dense accelerated-compute clusters on bare metal and Kubernetes, stitched together with high-performance Ethernet and InfiniBand fabrics, and deliver them to customers as reliable, secure, high-throughput platforms. They are building Cloud v2, their next-generation, clean-slate, Kubernetes-native GPU cloud, and scaling it across new data center sites. The infrastructure team sits at the center of that effort, building the systems that turn racks of hardware into a product customers depend on.
 

The role

We are hiring a Senior Observability Engineer to build the observability layer for Cloud v2. This role exists to close the single most damaging gap in our client’s current product: today their observability is reactive, so customers are their failure detector. Some of their worst incidents went undetected internally because they occurred below the layer they instrument (a corrupted forwarding entry, a flapping firewall sync process), and the customer reported the outage before their dashboards did. Cloud v2 is architected from day one against the industry’s Gold-tier bar for neocloud platforms (SemiAnalysis ClusterMAX), and proactive detection, where they see problems before the customer does, is the trust gap they are building this platform to fix.
 

You own the metrics, logs, alerting, dashboards, and detection stack that make that true. That means operator-facing observability that catches failures first and drives automated remediation, and tenant-facing observability that gives customers the usage, health, and status visibility a hyperscaler-grade cloud implies. This is the observability workstream the platform validates earliest, because detection is where the current product loses trust, and it maps directly to the ClusterMAX Monitoring and Reliability criteria.
 

This is a build-focused, hands-on senior IC role. You will spend your time engineering the telemetry pipelines, detection logic, dashboards, and alerting that turn raw signals from multi-vendor GPUs (AMD/NVIDIA), hosts, fabrics, and switches into problems we catch before customers feel them, and you will set the technical direction and standards for how we instrument the platform. You will partner closely with our client’s SRE, platform, network, and security engineers, who own the control-plane architecture, the fabric, and day-to-day operations; you own the observability that makes the whole platform legible and its failures visible.
 

What you’ll own

The OpenTelemetry foundation: own OpenTelemetry as the instrumentation standard the whole platform plugs into: the collector topology and pipelines, semantic conventions for metrics, logs, and traces, and consistent resource and tenant attribution across GPU, host, fabric, switch, and control-plane signals. Everything we instrument, internal and tenant-facing, lands on this common backbone so signals are portable, correlated, and not locked to any one backend.

Proactive detection: build the detection that fires before a customer reports, GPU failure-rate tracking, Xid detection, and the anomaly and threshold signals that feed the automated detect-drain-remediate loop, so mean time to detect stops being inverted and our client sees the problem first.

The metrics pipeline: own the metrics stack across GPU, CPU, network planes, thermal, and power (Prometheus and OpenTelemetry-based), including the DCGM profiling-metric suite (DCGM_FI_PROF_* for SM activity and tensor TFLOPs, plus PCIe AER, ECC, power and thermal, and NVLink and fabric throughput), with custom exporters and OpenTelemetry receivers where DCGM falls short.

Instrumenting below the layer we watch today: push instrumentation down into the sub-layers where our most damaging silent failures live (forwarding state, firewall and HA sync, fabric health), so the failure classes that used to reach us through the customer surface on our own dashboards instead.

Host and switch debug data: build reliable extraction of host and switch debug and telemetry data so responders have the signal they need without hand-collecting it during an incident.

Operator and tenant dashboards and alerting: build the operator dashboards and low-noise, actionable alerting the on-call team runs on, and a tenant-facing status page so customers are not our failure detector.

Tenant-scoped observability: deliver per-tenant usage and utilization visibility and per-tenant access to logs and metrics, cleanly isolated from internal ops telemetry, on a multi-tenant model where no tenant can see another’s data.

Proactive customer notifications: build automated, proactive maintenance and change notifications to affected tenants, replacing today’s manual and ad hoc comms.

Reliability and SLO instrumentation: build the telemetry behind the 99.9%+ node SLA measurement, the passive and active health-check battery, and MTTA and MTTR tracking, so reliability is measured, not asserted, and so the health automation has trustworthy signal to act on.

AI-leveraged observability: push our observability toward an AI-augmented model, agentic triage, anomaly detection, alert-noise reduction, and MCP-driven access to our operational telemetry, so a small team catches more, faster, than headcount alone would allow.

Technical direction and standards: set the instrumentation patterns, metric and label conventions, alerting standards, and SLO practice the rest of the platform adopts, and raise the bar through design review and mentorship.
 

What we’re looking for

Required

8+ years building production observability or monitoring systems at scale, ideally for cloud, hosting, or high-performance compute environments, with real platform-build experience (not solely operating dashboards someone else built).

Deep expertise with Prometheus and the metrics ecosystem: PromQL, exporters, recording and alerting rules, Alertmanager, and Grafana, used to build durable, reusable observability rather than one-off dashboards.

Hands-on experience designing and running OpenTelemetry as a foundational instrumentation layer: the Collector, receivers and exporters, semantic conventions, and vendor-neutral pipelines that multiple backends and teams plug into.

Experience running time-series at scale, including long-term and high-cardinality storage (Thanos, Mimir, Cortex, or VictoriaMetrics), and a real understanding of cardinality, retention, and query-performance tradeoffs.

A track record of building alerting that is actionable and low-noise, and of practicing SLO and error-budget-based reliability rather than alert on everything.

Strong programming skills in a real language (Python, Go, or similar) used to build maintainable exporters, collectors, and observability tooling, not just glue scripts.

Strong Linux systems fundamentals: you can reason about the OS, networking, storage, and the boot and provisioning path, and instrument at that level, not just at the application surface.

A track record of owning significant observability systems end to end and setting technical direction that other engineers adopt: defining standards and influencing architecture across a team.

A demonstrated bias toward building things right the first time: reproducible, code-defined observability and sound instrumentation design over short-term shortcuts.

Strong written communication: you document designs and decisions clearly, and you raise the team’s bar through design review and mentorship.
 

Strongly preferred

Multi-vendor GPU/HPC observability: DCGM & dcgm-exporter (NVIDIA) and RDC & AMD Device Metrics Exporter (AMD/ROCm); NVML and AMD SMI; Xid vs. amdgpu/RAS error reporting; DCGM_FI_PROF_* and ROCProfiler profiling metrics; GPU node health checks (DCGM diagnostics, AGFHC, ROCm Validation Suite).

Experience building multi-tenant, tenant-facing observability with per-tenant metric and log isolation.

Checkmk (our client runs parent and child sites today), and experience migrating or bridging a Checkmk estate into a modern metrics stack.

Network and fabric observability: switch telemetry, streaming telemetry (gNMI) and SNMP, BGP and fabric health, and RoCE Ethernet or InfiniBand/UFM fabric monitoring.

Log and trace pipelines and distributed tracing: Loki, Elastic, or Vector, and instrumenting traces across a distributed control plane.

Kubernetes observability: kube-state-metrics, cAdvisor, and GPU Operator metrics, in a multi-cluster environment.

Status-page and incident tooling, MTTA and MTTR instrumentation, and integration with an incident-response and paging workflow.

Building observability within a SOC 2, ISO 27001, or HIPAA compliance posture, including audit-log export.
 

How we work

Small, senior, high-trust teams. We hire people who can own and solve problems end to end and we give them the room to do it.

Code over clicks. The platform is defined, reviewed, and versioned. Manual changes are the exception and they get automated away.

Build it right. We invest in sound architecture and durable design so the platform scales cleanly instead of accumulating debt.

Customers are real. The platform exists to serve customer workloads, and we build to the reliability and self-service standard a hyperscaler-grade cloud implies.

We document. Designs, decisions, and architecture are written down so the team compounds knowledge instead of re-learning it.
 

AI as a force multiplier

We expect our engineers to use AI to move faster and operate at higher leverage, and we build our environment to make that the default rather than an afterthought. We maintain CLAUDE.md and AGENTS.md context files across our repositories, run MCP servers against our operational systems (Jira, NetBox, Checkmk), and use agentic coding tools such as Claude Code directly in our infrastructure and platform workflows.
 

For this role in particular, observability is one of the highest-leverage places to apply AI: anomaly detection, automated incident triage, alert-noise reduction, and AI-assisted root-cause analysis over the telemetry you build. You don’t need to be an AI expert, but you should be eager to fold these tools into how you design and operate observability, and to help us define what an AI-leveraged infrastructure team can do.

What success looks like
 

First 30 days

Ramped on the Cloud v2 architecture, the current observability stack (Prometheus, Checkmk parent and child sites, and the GPU and fabric telemetry), and the lab, and able to navigate the repos and design docs independently.

Shipped your first meaningful contribution to the metrics, alerting, or detection stack with the team’s review.

Aligned with SRE, platform, network, and security on where your work meets theirs and on the highest-value detection gaps to close first.
 

First 90 days

Owning a defined area of the v2 observability layer end to end, from design through delivery (for example the OpenTelemetry instrumentation foundation, the GPU and DCGM metrics pipeline, the alerting and SLO framework, or the tenant-facing dashboards and status page).

Delivered proactive detection for at least one failure class that the platform previously learned about from customers, so our client now detects it first.

Measurably reduced alert noise or improved mean time to detect for a meaningful part of the fleet.
 

First 12 months

The go-to engineer for the hardest observability and detection problems in Cloud v2, trusted across SRE, platform, network, and security, and setting the standards the team instruments by.

Designed and delivered a significant piece of the platform’s observability (proactive detection, tenant-facing observability, or reliability and SLO instrumentation) that raised the whole team’s leverage and moved the platform toward its Gold-grade Monitoring and Reliability bar.

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Middle-level Software Engineer to work on the core query engine of a large-scale distributed data platform. You will develop features across query planning, optimization, and execution, contribute to performance-critical components, and help investigate and fix production issues reported by real enterprise customers.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities

• Develop and maintain features across the query engine — planning, optimization, execution, and data access layers
• Write performance-conscious code in Java and/or C++
• Investigate and fix production and customer-reported issues under the guidance of senior engineers, including fixes delivered across multiple supported release lines
• Work with SQL semantics, query plans, and execution operators over large-scale distributed data
• Integrate with columnar formats, open table formats, and connectivity drivers
• Contribute to CI/CD and automated testing in Jenkins
• Deploy and validate changes on Kubernetes (GKE/EKS/AKS) across GCP, AWS, or Azure with Docker
• Debug issues across query planning, distributed execution, memory management, and I/O; collaborate with US-based teams on design and code reviews
•  

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field
• 3+ years in backend / systems software engineering
• Strong proficiency in Java or C++ with solid OOP and software design fundamentals, including concurrency and asynchronous programming (comfort with both languages is especially valued)
• Strong SQL and understanding of relational and analytical data systems, including query execution concepts
• Hands-on experience with data processing systems: query engines, distributed databases, ETL/ELT, or analytical platforms
• Experience with Jenkins pipelines and modern development workflows
• Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency)
• Hands-on experience with at least one major cloud (GCP, AWS, or Azure)
• Confident Git/GitHub workflows
• Comfortable with AI-assisted development workflows — using modern AI tools for code comprehension, debugging, and test generation, and critically validating their output
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team

• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

   

Desired Skills

• Apache Arrow (columnar in-memory format) and SQL planner/optimizer frameworks such as Apache Calcite
• LLVM-based runtime expression compilation
• Open table formats — Apache Iceberg, Delta Lake, or Hudi — and columnar file formats such as Parquet, Avro, or ORC
• MPP query engines (Presto, Trino, or similar); exposure to distributed data platforms such as Spark, Snowflake, or Databricks
• Messaging systems: Kafka, NATS, or cloud pub/sub services
• Query planning, optimization, and execution internals
• Connectivity drivers: JDBC, ODBC, Arrow Flight
• Managed Kubernetes (GKE/EKS/AKS), multi-cloud exposure; Terraform

• Performance profiling of latency-sensitive systems

   

Details

• Engagement: Long-term contract
• Location: Europe (EU / EEA / UK), remote
• Working hours: EU business hours, shifted 2–3 hours later for daily overlap with the US West Coast team

We are looking for a Senior Data Engineer to join an experienced technical team working on modern data platforms and large-scale data processing.

The role combines data architecture, ETL, data warehousing, and big data / real-time pipelines, with solutions running both on-premises and in the cloud.
 

What you’ll do

• Design and implement data models for complex data environments
• Develop and maintain ETL/ELT processes
• Build and optimize big data and real-time data pipelines
• Work with data platforms across on-premises and cloud environments
• Design and implement solutions for data warehousing and data movement
• Work with relational databases and large datasets
• Optimize queries and data processing workflows
• Develop data engineering solutions using SQL and Python
• Collaborate with other engineers and technical stakeholders on data architecture and implementation
   

Requirements

• 3–5+ years of experience in Data Engineering
• Strong experience with database technologies and data architecture
• Hands-on experience with Oracle — especially relevant for this role
• Experience with data warehousing
• Experience with ETL and data movement technologies
• Excellent SQL skills
• Strong Python development skills
• Experience building big data and/or real-time data pipelines
• Understanding of both on-premises and cloud-based data environments
• Strong analytical and problem-solving skills
• Fluent English — required for daily communication
   

Education

• Master’s degree in Computer Science or a comparable technical qualification
   

What you’ll work with

• Oracle and other database technologies
• SQL
• Python
• ETL / ELT
• Data warehousing
• Big Data pipelines
• Real-time data processing
• On-premises and cloud infrastructure
   

What We Offer

• Paid vacation and sick leave
• Equipment to work comfortably, if required
• 50% coverage of professional development courses
• Regular salary reviews based on performance
• Competitive salary, depending on experience
• Always on-time payments — no delays
• A supportive team without unnecessary bureaucracy

As a Data Engineer on the Enterprise Data & Intelligence team, you will design, build, and maintain the data pipelines and models that underpin Fourth’s single source of truth. Reporting to the Senior Data Engineer, you will gather and translate business requirements into reliable, well-tested data solutions on our Azure-based cloud platform. You will collaborate closely with analytics engineers and business stakeholders to scope, prioritise, and deliver data products that serve FP&A, Commercial, Product, and AI teams.

Responsibilities:

1. Data Pipeline Development & Optimisation

•Build, maintain, and optimise scalable ETL/ELT pipelines (batch and near-real-time) on Azure Data Cloud Platform (e.g., Data Lake, Microsoft Fabric, Azure Data Factory).

• Develop and refine data models to support BI reporting, analytics, and ML/AI use cases.

•Write efficient, well-documented T-SQL and PySpark code following team coding standards.

2. Data Quality, Testing & Governance

• Implement automated testing, data validation, and monitoring (SLAs, alerts) to ensure pipeline reliability.

• Contribute to data governance practices, including lineage tracking, metadata management, and quality controls.

•Support CI/CD pipelines for data assets, ensuring version control and reproducibility.

3. Requirements Scoping & Cross-Functional Collaboration

•Partner with analytics engineers to scope, refine, and prioritise data requirements from business stakeholders.

•Work with Analysts, BI Developers, Data Scientists, and business teams to translate requirements into production-ready data solutions.

•Provide input on data readiness for machine learning and analytics projects.

4. Platform Contribution & Continuous Improvement

• Contribute to the evolution of the ED&I data platform, including tooling, standards, and documentation.

• Stay current with emerging data engineering patterns and technologies; propose improvements to team processes.

• Leverage AI-driven development tools (e.g., generative-AI code assistants, automated data profiling) to accelerate delivery.

• Support performance tuning and cost optimisation across the data platform.

Requirements:

Experience: 3+ years in data engineering or a closely related role.

Education: Bachelor’s degree in Computer Science, Data Engineering, or a related field.

Technical Skills:

○Strong T-SQL skills and working proficiency in PySpark or Python for data processing.

○Hands-on experience with MS Azure Storage Explorer and SSMS.

○Hands-on experience with cloud-based data engineering services and orchestration tools (e.g., Azure Data Factory, Microsoft Fabric).

○Practical experience building ETL/ELT pipelines and dimensional or analytical data models.

○Familiarity with CI/CD practices in data engineering, including version control (Git) and automated testing.

Preferred Qualifications
• Experience with real-time or streaming data architectures.
• Experience with PowerShell, Apache Kafka, and/or KQL.
• Exposure to AI/ML workflows (feature engineering, data preparation for model training).
• Familiarity with Power BI or other BI/visualisation tools.
• Experience using AI productivity tools (e.g., ChatGPT, Claude, Copilot, Cursor) in day-to-day and data engineering tasks.
• Understanding of data security, privacy, and compliance considerations.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers
 

We're looking for a Middle+ Data/Databricks Engineer to join our data team and take ownership of pipelines built on Databricks. You'll design and run production-grade ETL/ELT workloads, work with the lakehouse (Delta Lake) architecture, and partner closely with analysts, data scientists, and business stakeholders who rely on the data you deliver. "Middle+" here means roughly 3–5 years of hands-on data engineering experience: you can own a pipeline end to end with minimal supervision, make sound design decisions, and are starting to mentor more junior engineers — without yet carrying full architectural ownership of the platform.

Contract & Logistics

• Contract type: B2B (valid Czech IČO / trade license required).
• Location: Remote, candidate must be based in the Czech Republic; occasional visit to Prague office.
• Start date: ASAP
• English: Fluent 
• Czech: Native/Fluent 

What You'll Do

• Design, build, and maintain scalable ETL/ELT pipelines on Databricks using PySpark and SQL.
• Implement and evolve Delta Lake tables following a medallion (bronze/silver/gold) architecture.
• Develop batch and, where needed, streaming data processing jobs (Structured Streaming).
• Build and maintain CI/CD for Databricks notebooks and jobs (Databricks Asset Bundles, Repos, Git-based workflows).
• Orchestrate pipelines with Databricks Workflows, Azure Data Factory, Airflow, or similar tools.
• Monitor job performance and cluster costs; troubleshoot failures and optimize for reliability and spend.
• Implement data quality checks, testing, and monitoring for critical datasets.
• Collaborate with analysts, data scientists, and business stakeholders to translate requirements into data models.
• Participate in code reviews, document technical decisions, and help shape engineering best practices.
• Provide guidance and informal mentorship to junior team members.

What We're Looking For

• 3+ years of hands-on experience with Apache Spark, ideally on Databricks specifically.
• Strong PySpark and SQL skills, including performance tuning of Spark jobs.
• Practical experience with Delta Lake and lakehouse architecture concepts.
• Solid grasp of data modeling (dimensional modeling, star schema) and ETL/ELT design patterns.
• Experience with at least one major cloud platform — Azure, AWS, or GCP (Azure Databricks is a plus).
• Familiarity with pipeline orchestration tools (Databricks Workflows, ADF, Airflow, or equivalent).
• Working knowledge of Git and CI/CD practices for data engineering.
• Understanding of data governance, access control, and security basics (Unity Catalog is a plus).
• Good written and spoken English; Czech or Slovak is a plus but not required.
• Comfortable working independently and communicating clearly in a remote, distributed team.

Nice to Have

• Databricks certification (Data Engineer Associate or Professional).
• Experience with streaming technologies such as Kafka or Azure Event Hubs.
• Experience with dbt for transformation and testing.
• Solid Python software-engineering practices — testing, packaging, typing.
• Infrastructure-as-code experience (Terraform).
• Exposure to MLOps or ML pipeline support.
• Experience in a regulated or large enterprise data environment.

What We Offer

• Fully remote work with flexible hours, based anywhere in the Czech Republic.
• Long-term B2B cooperation with a stable pipeline of work.
• A modern, actively evolving data stack rather than legacy maintenance work.
• Direct collaboration with senior engineers and architects — real input on design decisions.
• Budget for training, certifications, and conference attendance.
• A flat structure with fast decision-making and minimal bureaucracy.

We're Gypsy Collective. The digital world is evolving faster than ever. New opportunities, new challenges, new ambitious goals. That’s why strong businesses need strong people. 

Gypsy Collective is an exclusive talent partner for selected brands. We help our partners attract exceptional talent by building long-term relationships and becoming deeply integrated into their business, culture, and growth ambitions.

We are looking for a DWH Engineer to design, build, and maintain scalable data warehouse solutions that support analytics and business decision-making. You will work across the full data lifecycle - from data ingestion and transformation to orchestration and optimization - while collaborating with analysts, engineers, and business stakeholders.
 

📅  Your adventures include:

• Develop, and maintain DWH and Data Lake solutions aligned with business requirements;
• Build, optimize, and support ETL/ELT pipelines using Python, SQL, Airflow, and related technologies;
• Integrate and maintain data flows from APIs, databases, SaaS platforms, and third-party systems;
• Work with Trino and cloud-based analytical platforms to support reporting and analytics needs;
• Implement and support incremental loading, CDC processes, backfills, and reprocessing workflows;
• Monitor, troubleshoot, and improve data pipelines to ensure reliability, stability, and timely delivery of data;
• Implement data quality checks, validation rules, and automated monitoring processes;
• Optimize SQL queries, data models, and pipeline performance;
• Collaborate with analysts, BI developers, engineers, and business stakeholders to deliver scalable data solutions;
• Participate in code reviews and contribute to engineering standards and best practices.

🧭 What makes you a great match:

• Strong SQL skills, including complex queries, CTEs, window functions, analytical queries, and query optimization (2+ years);
• Strong experience integrating external data sources, including REST APIs, databases, SaaS platforms, and third-party systems;
• Experience with Apache Airflow (2+ years);
• Experience with Python for data processing, integrations, and automation (2+ years);
• Good understanding of DWH concepts, including ETL/ELT, dimensional modeling, Data Vault, and Star/Snowflake schemas (2+ years);
• Experience building and supporting production data pipelines with a focus on reliability, stability, monitoring, and error handling;
• Understanding of data quality principles, including validation, testing, reconciliation, and monitoring;
• Experience with Git

Benefits

💸 Flexible payment options: choose the method that works best for you;
🎁 Financial perks: Bonuses for holidays, B-day, work milestones and more - just to show we care;
📈 Learn & grow: We cover courses and certifications — and offer real opportunities to grow your career with us;
🥐 Benefit Сafeteria: Choose what suits you — sports, language courses, therapy sessions, and more;
🎉 Stay connected: From team-building events to industry conferences — we bring people together online, offline, and on stage;
💻 Modern Equipment: We provide new laptops along with essential peripherals like monitors and headphones for a comfortable workflow;
🕘 Your schedule, your rules: Start your day at 9, 10, or even 11 — we care about results, not clock-ins.