Jobs Data Engineer

The project is for one of the world's famous science and technology companies in pharmaceutical industry, supporting initiatives in AWS, AI and data engineering, with plans to launch over 20 additional initiatives in the future. Modernizing the data infrastructure through the transition to Snowflake as a priority, as it will enhance capabilities for implementing advanced AI solutions and unlock numerous opportunities for innovation and growth.

We are seeking a highly skilled Snowflake Data Engineer to design, build, and optimize scalable data pipelines and cloud-based solutions across AWS, Azure, and GCP. The ideal candidate will have strong expertise in Snowflake, ETL Tools like DBT, Python, visualization tools like Tableau and modern CI/CD practices, with a deep understanding of data governance, security, and role-based access control (RBAC). Knowledge of data modeling methodologies (OLTP, OLAP, Data Vault 2.0), data quality frameworks, Stream lit application development and SAP integration and infrastructure-as-code with Terraform is essential. Experience working with different file formats such as JSON, Parquet, CSV, and XML is highly valued.

• Responsibilities:

  • In-depth knowledge of Snowflake's data warehousing capabilities.
  • Understanding of Snowflake's virtual warehouse architecture and how to optimize performance
  and cost.
  • Proficiency in using Snowflake's data sharing and integration features for seamless collaboration.
  • Develop and optimize complex SQL scripts, stored procedures, and data transformations.
  • Work closely with data analysts, architects, and business teams to understand requirements and
  deliver reliable data solutions.
  • Implement and maintain data models, dimensional modeling for data warehousing, data marts,
  and star/snowflake schemas to support reporting and analytics.
  • Integrate data from various sources including APIs, flat files, relational databases, and cloud
  services.
  • Ensure data quality, data governance, and compliance standards are met.
  • Monitor and troubleshoot performance issues, errors, and pipeline failures in Snowflake and
  associated tools.
  • Participate in code reviews, testing, and deployment of data solutions in development and production environments.

• Mandatory Skills Description:

  • 5+ years of experience
  • Strong proficiency in Snowflake (Snowpipe, RBAC, performance tuning).
  • Ability to write complex SQL queries, stored procedures, and user-defined functions.
  • Skills in optimizing SQL queries for performance and efficiency.
  • Experience with ETL/ELT tools and techniques, including Snowpipe, AWS Glue, openflow, fivetran
  or similar tools for real-time and periodic data processing.
  • Proficiency in transforming data within Snowflake using SQL, with Python being a plus.
  • Strong understanding of data security, compliance and governance.
  • Experience with DBT for database object modeling and provisioning.
  • Experience in version control tools, particularly Azure DevOps.
  • Good documentation and coaching practice.

We’re building a next-generation payment platform from the ground up (0→1) — and we’re looking for a Senior Payment Infrastructure Engineer with deep expertise in fiat/card payments to take full ownership of the core system.

This is not about plugging APIs. This is about building real payment infrastructure.

🚀 What You’ll Do

• Build and own core fiat/card payment infrastructure
• Integrate with acquirers, PSPs, and payment processors
• Design and debug end-to-end payment flows (auth, capture, settlement, refunds)
• Work with 3DS2, MIT, recurring payments, and card-on-file systems
• Architect scalable billing and payment systems from scratch
• Take full ownership as the first (solo) engineer in this domain

🧠 What We’re Looking For

• 5+ years of hands-on production experience in card payments
• Strong experience with PHP and/or Go
• Deep understanding of payment lifecycle and card network specifications
• Practical experience with:
  • 3DS2
  • Merchant-initiated transactions (MIT)
  • Recurring payments
  • Card-on-file flows
• Strong debugging skills (request/response level, transaction tracing)
• Ability to build systems independently from 0→1
• AI-native engineering mindset 🤖

⭐ Bonus Points

• Experience as MoR (Merchant of Record) or PayFac
• Multi-acquirer / international payment setups
• Experience building payment infrastructure (not just integrations)
• Startup or early-stage product experience

💼 What We Offer

• Full ownership of a core payment platform
• High-impact engineering work with zero bureaucracy
• Opportunity to build critical infrastructure from scratch
• AI-first engineering environment

On behalf of our client, we are looking for a Senior Infrastructure Engineer (GPU Platform)

Requirements:

- Production experience with multi-node GPU training infrastructure

- Strong Linux, containers, CUDA, and NVIDIA GPU stack knowledge

- Hands-on experience with NCCL and InfiniBand or RoCE/RDMA troubleshooting

- Deep experience with Kubernetes or Slurm

- Experience with infrastructure automation and observability

- Experience diagnosing issues across training workloads, networking, storage, and GPU hosts

- Strong incident leadership and provider-facing communication skills

- English – Upper-Intermediate or higher

Would be a plus:

- Experience in an AI lab, HPC environment, or specialist GPU cloud

- PyTorch, Megatron, DeepSpeed, or other distributed-training frameworks

- Experience with parallel storage and checkpoint optimization

- Experience working with multi-provider GPU platforms

Company offers:

- Long-term employment with possibilities for professional growth

- Fully remote work

- Reasonably flexible schedule

- 15 days of paid vacation

- Regular performance reviews

The Mission
Are you a cloud architect who finds beauty in a perfectly orchestrated pipeline? Do you believe that “manual” is a four-letter word? We are looking for a Senior SRE specialized in BI Ecosystems to join our mission-critical Infrastructure team.
In this role, you won’t just “maintain” systems; you will be the master weaver connecting our advanced Data Analytics tools with a rock-solid, automated GCP/AWS backbone. You will bridge the gap between traditional Business Intelligence and modern Cloud Engineering, ensuring our data flows are fast, our visualizations are seamless, and our infrastructure is invisible.

What You Will Do
1. Master the Orchestration (GCP & GKE)
You will take full ownership of our Google Kubernetes Engine (GKE) clusters. This isn’t just
about pods and services; you’ll manage advanced networking (VPC Service Controls, Shared VPC) and high-availability node configurations to ensure our data platforms never skip a beat.
2. Advance Data Architectures (Lakehouse)
You will design and support a unified Data Lakehouse architecture by integrating BigQuery and Cloud Storage. Your mission is to break down silos, ensuring that our raw data and analytical models coexist in a single, high-performance ecosystem. You will implement robust governance and security policies that ensure data quality and accessibility, providing a “single source of truth” that is both scalable and cost-effective
3. Evangelize Infrastructure as Code (IaC)
Our goal is 100% immutability. You will use Terraform to automate the entire lifecycle of our
GCP resources—from Compute Engine and Cloud Run to Cloud SQL—ensuring consistency
across Dev, Staging, and Production.
4. Empower DataViz Excellence
You will be the enabler for our Data Analysts. You’ll deploy, optimize, and scale Qlik Sense,
Tableau, or Similars environments on GCP, handling everything from SSO/IAM authentication to high-performance connectivity with our backend data sources.
5. Hybrid Cloud & Legacy Modernization
Complexity is our playground. You will manage critical AWS resources (EC2, S3) and ensure
seamless interoperability with GCP. Simultaneously, you will lead the charge in migrating legacy monolithic systems into modern, microservices-based architectures.
6. Data Engineering Collaboration
Working side-by-side with Data Engineers, you’ll help build and maintain robust pipelines using Cloud Dataflow, Cloud Composer (Airflow), and BigQuery. You ensure that the “plumbing” of our data world is leak-proof and high-performing.
7. Reliability, Security & FinOps
● CI/CD: Design sophisticated pipelines (GitLab CI/GitHub Actions) integrating DevSecOps best practices.
● Observability: Implement deep monitoring using Google Cloud Operations Suite and Grafana.
● FinOps: Be the guardian of our budget, identifying and eliminating cloud waste through
smart optimization.

Who You Are (Requirements)
● Cloud Expert: Proven experience managing complex environments in Google Cloud Platform (GCP) and AWS.
● Data Architect: Deep understanding of Data Lakehouse models and governance.
● Kubernetes Pro: Deep knowledge of GKE, including advanced networking and security policies.
● Terraform Ninja: Ability to write modular, scalable, and secure IaC.
● BI Infrastructure Savvy: Experience deploying and tuning enterprise visualization tools (Tableau, Qlik, or Power BI).
● Automation Mindset: Strong scripting skills (Python, Bash, or Go) and mastery of CI/CD engines.
● Data-Driven: Familiarity with Big Data tools like Airflow, BigQuery, and Dataflow.
● Security-First: Solid understanding of encryption, IAM roles, and network security standards.

Why Join Us?
● Impact: You are not a cog in the machine; you are building the machine.
● Tech Stack: Work with the latest tools in the Cloud and Data world (Lakehouse, GKE).
● Culture: We value technical excellence, proactive problem-solving, and a “fail fast, learn
faster” mentality.
● Growth: We invest in your certifications and continuous learning. Ready to redefine how BI meets SRE? If you are a builder who loves solving the puzzle of high-scale data infrastructure, we want to meet you.

Apply now and let’s build the future of data infrastructure together!

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.

Key Responsibilities  

• Design, build, and maintain scalable, reliable data pipelines (batch and streaming).  
• Develop and optimise data models, warehouses, and lakehouse architectures.  
• Build and automate CI/CD pipelines for data workflows and deployments.  
• Ensure data quality, integrity, lineage, and governance across environments.  
• Implement proactive monitoring and alerting for pipelines and data infrastructure (CloudWatch, Grafana).  
• Manage IaC for consistent, reliable data infrastructure.  
• Troubleshoot and optimise pipeline and query performance across environments.  
• Lead incident resolution and root cause analysis for data systems.  
• Collaborate with analysts, data scientists, and engineering teams, ensuring security, scalability, and operational excellence to understand data requirements and translate them into technical solutions  
• Document data architecture, lineage, and workflows  

Must have  

• 5+ years in data engineering, with solid DevOps/platform engineering exposure.  
• Strong SQL and data modelling skills (dimensional, normalised, lakehouse).  
• Proficient in Python; working knowledge of Scala, Java, or Go.  
• Hands-on experience with data pipeline/orchestration tools (Airflow, DBT).  
• Experience with distributed processing frameworks (Spark, Flink, or similar).  
• Advanced AWS expertise (Glue, EMR, Redshift, Athena, S3, Lambda, Kinesis).  
• CI/CD tools (GitHub Actions, GitLab CI, Jenkins, CircleCI).  
• Infrastructure as Code (CDK, CloudFormation).  
• Experience with streaming/event-driven architecture (Kafka, Kinesis, Apache Flink, or Pub/Sub).  
• Observability tools: Grafana, CloudWatch.  
• Expertise with data warehouse platforms (Redshift, Snowflake).  

Desirable  

• Experience with containerisation and orchestration (Docker, ECS, Kubernetes).  
• Understanding of data governance, cataloguing, and security/compliance (GDPR, lineage tools).  
• Experience with GitHub Enterprise, Atlassian APIs, and Slack bots.  
• Understanding of cost optimisation strategies in multi-cloud and data warehouse environments.  

We are currently looking for a Middle+ / Senior Data Engineer to join our Analytics Department and contribute to the development of the company’s analytical platform.

The main focus of the role is developing and optimizing our ClickHouse-based Data Warehouse, designing reliable ETL/ELT processes, and improving the overall data architecture.

Your main goal: build and maintain a scalable, reliable, and high-performance data infrastructure that supports the company’s analytics and business needs.

What experience is important to us:

• At least 3 years of experience in Data Engineering, Analytics Engineering, DWH development, or a similar role.
• Expert-level ClickHouse: DWH schema design, engines, partitioning, indexing, Materialized Views, optimization of complex JOINs and aggregations, system log analysis.
• Advanced SQL skills.
• Strong Python skills for pipeline development and automation.
• Hands-on experience with Apache Airflow, including DAG development, maintenance, and monitoring.
• Experience with REST APIs, Webhooks, and S3-compatible storage (MinIO / AWS S3).
• Confident knowledge of Linux, Git/GitHub, and Docker.
• Experience with Prometheus & Grafana.
• English at B1–B2 level.
• Strong systems thinking, independence, and ownership.

Will be a plus:

• Experience in iGaming, FinTech, or E-commerce.
• Experience designing and building a DWH from scratch.
• Experience with high-load ClickHouse environments.
• Basic understanding of Apache Kafka.

What you will do:

• Develop and maintain the company’s ClickHouse-based DWH, including architecture, data layers, and data marts.
• Design and optimize storage schemas and high-load analytical processes.
• Develop and maintain ETL/ELT pipelines in Apache Airflow and ensure their reliability.
• Optimize resource-intensive processes and analytical queries.
• Build and maintain integrations with internal and external data sources.
• Work closely with business, analytics, and engineering teams to formalize requirements and implement solutions.
• Handle ad hoc data requests and research datasets.
• Improve data engineering standards, maintain technical documentation, and reduce technical debt.

The position is full-time and fully remote. Due to security requirements, daily work is performed within a protected environment via remote desktop (WDS/RDS).

Interested?

If you have strong expertise in ClickHouse, SQL, Python, and Airflow, enjoy solving complex data infrastructure challenges, and want to influence how a growing analytical platform is built and scaled — we’d love to talk.

Apply for the role or reach out directly to learn more about the team, infrastructure, and challenges.

Project description

We are looking for a Regular DevOps / Cloud Infrastructure Engineer to support infrastructure automation, environment deployment, and GitOps-based delivery.

Responsibilities

Own the Terraform infrastructure

API Gateway routes, Lambda functions, IAM roles, EC2, DynamoDB tables, SQS queues, AWS Workspaces .etc

Design and maintain Python Lambda handlers that integrate with Active Directory/LDAP, GitLab, JFrog Artifactory, and the AWS SDK, handling KMS-encrypted secrets and SSM parameters.

Run and troubleshoot the Jenkins deployment pipeline across environments (plan / create / destroy / test).

Keep integration tests healthy and add coverage for new endpoints.

Manage secrets and access control

Jenkins credentials, KMS key permissions, API keys stored in SSM.

Solid Ansible skills for authoring and maintaining idempotent playbooks, roles, and inventories across Linux and Windows hosts.

Strong Linux administration skills, including shell scripting, package and service management, systemd, networking, and troubleshooting.

Skills

Must have

Hands-on experience with AWS or Azure.

Experience with Terraform and infrastructure-as-code.

Knowledge of networking: VPC, subnets, routing, security groups, NAT, private endpoints.

Experience with IAM roles and policies.

Experience with Jenkins, GitLab CI/CD or Azure DevOps Pipelines.

Ability to troubleshoot complex cloud deployments.

Good communication and documentation skills.

Nice to have

PowerShell for Windows / AD / Workspaces helper scripts.

JFrog Artifactory REST API

permissions, replication, tokens, storage.

Windows Server administration, RSAT, and SSM Run Command.

Bedrock and SageMaker service familiarity.

Experience: 4+ years
Project Duration: September 14, 2026 – September 1, 2027
Format: Remote
Location: Poland

About the Project

We are looking for a Data Engineer to join a core data team supporting a strategic client engagement.

The role combines application maintenance and support (AMS) with new development, providing an opportunity to work across the full data lifecycle. The main focus will be on data transformation, data warehousing, and dimensional modeling within a modern cloud-based data platform.

You will work closely with business stakeholders to understand data requirements, translate them into technical solutions, and deliver high-quality, scalable data products.

Responsibilities

• Work across the full data lifecycle, combining AMS activities with new development
• Collaborate with business stakeholders to understand data requirements and translate them into technical solutions
• Develop scalable and high-quality data products within a cloud-based data platform
• Perform data transformation, data warehousing, and dimensional modeling
• Apply Data Vault 2.0 concepts and data warehouse design principles
• Contribute to data integration and ETL/ELT processes
• Analyze and optimize SQL queries and data processing performance

Requirements

• 4+ years of experience in Data Engineering or Data Warehousing projects
• Strong hands-on experience with Snowflake
• Advanced SQL skills, including complex query development and performance optimization
• Experience with data transformation and dimensional modeling
• Knowledge of Data Vault 2.0 methodology and data warehouse design principles
• Ability to gather business requirements and translate them into effective technical solutions
• Experience working directly with business stakeholders and non-technical users
• Familiarity with data integration and ETL/ELT processes
• Strong analytical and problem-solving skills
• Good English communication skills (minimum B2, preferably C1)

Tech Stack

• Data Engineering
• Data Warehousing
• Snowflake
• SQL
• Data Vault 2.0
• ETL / ELT

Шукаємо Senior / Lead Software Engineer (Data), який допоможе будувати й масштабувати production-grade data systems: пайплайни, сервіси та storage-рішення для збору, обробки, зберігання й доставки даних. Це роль для інженера з сильним software engineering mindset, який вміє працювати не лише на рівні реалізації, а й на рівні архітектури, data modeling, reliability, performance та technical leadership. Очікуємо, що людина зможе брати end-to-end ownership за критичні data workflows і впливати на стандарти команди.
 

Обовʼязки:

• Проєктувати, розробляти та підтримувати batch/streaming data pipelines і data services.
• Приймати технічні рішення щодо ingestion, orchestration, storage, трансформацій і delivery.
• Будувати ETL/ELT-рішення з використанням Python, Airflow, Cron.
• Інтегрувати дані з API, логів, NoSQL, IoT/embedded-джерел та внутрішніх сервісів.
• Проєктувати схеми зберігання, моделі даних і структури баз даних.
• Оптимізувати рішення з погляду performance, scalability, cost та maintainability.
• Забезпечувати data quality, observability, reliability і стабільність production-процесів.
• Планувати та реалізовувати міграції даних між платформами й форматами.
• Документувати схеми, трансформації, data lineage та ключові технічні рішення.
• Брати участь у code review, design discussions, incident resolution.
• Менторити інших інженерів і розвивати engineering standards у data-напрямку.
   

Вимоги:

• Значний досвід як Senior / Lead Software Engineer, Data Engineer або Backend Engineer у data-intensive середовищі.
• Сильний Python і досвід побудови production-ready рішень.
• Досвід роботи з Apache Airflow.
• Досвід роботи з MongoDB або іншими NoSQL-рішеннями.
• Сильне розуміння проєктування баз даних і data models: schema design, indexing, normalization/denormalization.
• Досвід роботи з великими обсягами даних, high-throughput або streaming workloads.
• Досвід із Azure Blob Storage або аналогічними object storage.
• Впевнене знання SQL та оптимізації запитів.
• Глибоке розуміння ETL/ELT, incremental processing, datalake/warehouse principles.
• Розуміння testing, observability, fault tolerance, idempotency, recovery для data systems.
• Досвід із Git, CI/CD, production deployment.
• Досвід технічного лідерства, mentoring, design review і роботи зі стейкхолдерами.
   

Буде плюсом:

• Snowflake, Elasticsearch
• Kafka або інші streaming-платформи
• Grafana / Prometheus
• Ansible або інші IaC / automation tools
• Power BI
• Досвід із cloud DWH, governance, lineage, internal data tools
  
  Надаємо бронювання*
  Офіс з повноцінним укриттям*

У зв’язку з активним розширенням шукаємо в команду Senior EO/IR Systems Engineer.

В основні обовʼязки буде входити:
 

• Формувати архітектуру EO/IR комплексу: EO, monochrome, LWIR/MWIR, LRF та інші сенсори.
• Визначати вимоги до сенсорів, оптики, FOV, resolution, focal length, NETD, SNR тощо.
• Порівнювати доступні на ринку модулі за ключовими характеристиками та обирати оптимальний варіант для конкретної системи (trade-off)
• Аналізувати ринок та взаємодіяти з виробниками.
• Проєктувати взаємне розташування сенсорів та систему юстування.
• Працювати над калібруванням, thermal drift та стабільністю оптичної системи.
• Організовувати лабораторні та польові випробування.
• Працювати разом із Mechanical, Embedded, Computer Vision, Controls та System Engineers.
• Формувати технічні вимоги та приймати рішення щодо оптичної архітектури.

У Вас має бути:

• 3+ років досвіду в EO/IR, electro-optics, optical systems, imaging systems.
• Сильне розуміння оптики та imaging systems.
• Практичний досвід вибору та інтеграції camera/sensor modules.
• Розуміння EO, LWIR/MWIR, LRF.
• Досвід роботи з boresight / calibration.
• Вміння самостійно проводити технічний аналіз та trade-off.
• Досвід роботи та інтеграції термальних, та монохромних камер.

Досвід лабораторних або польових випробувань.


Ми пропонуємо:

• Бронювання з першого робочого дня (відповідно до законодавства, за наявності необхідних документів).
• Офіційне працевлаштування, оплачувана відпустка, повна компенсація лікарняних згідно законодавству.
• Медичне страхування.
• Конкурентна винагорода.
• Участь в створенні амбітних та цікавих продуктів.
• Мінімум бюрократії — короткий шлях від ідеї до її реалізації.
• Ефективні рішення не відкладаються, а впроваджуються.
• Швидке професійне зростання у сфері defense-tech.
• Сучасна техніка для комфортної і продуктивної роботи.
• Комфортний офіс зі снеками та фруктами, а також паркомісця для співробітників (робота full-time в офісі).

Компанія Dataweb Global Group шукає спеціаліста на роль Infrastructure Engineer для розвитку, управління та оптимізації інфраструктури дата-центрів компанії, а також забезпечення високої доступності та надійності наших сервісів.

Dataweb Global Group — міжнародний провайдер хостингових та ІТ інфраструктурних послуг. Ми надаємо комплексні рішення для ІТ-інфраструктури на базі виділених серверів, які складаються з компонентів від постачальників світового рівня, хмарних сервісів та послуг з доставки контенту.

Ми працюємо з бізнесами, які потребують індивідуальних рішень для управління великими обчислювальними потужностями: великі VoD-платформи, Martech/ Adtech-платформи, Fintech, AI/ML тренування та обчислення, ігрова індустрія та багато інших.

Наша місія — надавати персоналізовані рішення як для бізнесу, так і для людей, які за ним стоять.

Основні обов’язки:

• Проєктувати, запускати та вводити в експлуатацію нові стійки, кейджі та приміщення в дата-центрах
• Налаштовувати та розвивати системи моніторингу інфраструктури
• Забезпечувати оптимальне використання ресурсів ДЦ: електропостачання, охолодження, простору
• Розробляти, впроваджувати та контролювати дотримання внутрішніх і галузевих стандартів (DCIM)
• Підбирати, тестувати та впроваджувати нове обладнання, інструменти та підходи
• Працювати з постачальниками, партнерами та провайдерами дата-центрів
• Консультувати внутрішні команди з інфраструктурних питань
• Проводити аудит та інспектування потенційних дата-центрів, готувати рекомендації
• Брати участь в on-call чергуваннях та реагуванні на аварійні ситуації

Вимоги:

• Досвід роботи з інфраструктурою дата-центрів та DCIM-системами
• Розуміння індустріальних стандартів та best practices у сфері дата-центрів
• Досвід роботи з системами моніторингу та візуалізації даних
• Знання основ фізики в контексті ДЦ: електрика, термодинаміка, охолодження
• Досвід роботи з системами електроживлення (UPS, PDU, ATS)
• Розуміння принципів охолодження серверів, стійок і дата-центрів
• Навички кабель-менеджменту та обліку інфраструктури
• Знання серверного та мережевого обладнання, досвід його експлуатації
• Розуміння принципів роботи телекоммунікаційного обладнання та транспортних систем за межами датацентрів, систем ущільненні зв’язку CWDM, DWDM.
• Розвинені комунікаційні навички та вміння працювати з кількома командами та підрядниками

Бажано:

• Досвід сертифікації або роботи з сертифікованою інфраструктурою ДЦ
• Досвід роботи з кількома комерційними дата-центрами
• Знання або практичний досвід роботи з Cloud / CDN-інфраструктурою
• Досвід участі в проєктах міграції або масштабування інфраструктури

Ми пропонуємо:

• Конкурентну оплату
• Можливість працювати з будь-якої точки Європи (надаємо технічну підтримку)
• 25 робочих днів на відпочинок щорічно, а також оплачувані лікарняні
• Бенефіти: обіди, страхування, курси англійської мови, корпоративні заходи та подарунки. Також ми активно підтримуємо воїнів ЗСУ і донатимо на потреби разом

Позиція компанії щодо ситуації в Україні

Будьмо сильними! І до зв’язку 🙂
Чекаємо вас у нашій команді.

Детальну інформацію про те, як Dataweb Global group використовує ваші персональні дані, можна знайти у Політиці конфіденційності кандидатів Dataweb Global group datawebglobal.com/en/documents/privacy

Огляд

Вакансія передбачає роботу у відділі аналітики Міноборони. Шукаємо розробника баз даних (Data Engineer) для проєктування, розвитку та супроводу аналітичної інфраструктури даних. Посада передбачає роботу з архітектурою БД, процесами завантаження та трансформації даних (ETL), побудовою вітрин даних і контролем якості інформації.

Обов’язки

• проєктування та розвиток структури баз даних (OLTP / OLAP)
• розробка та підтримка процесів ETL / ELT (автоматизація завантаження, очищення та трансформації даних)
• побудова та оптимізація вітрин даних (data marts) для аналітичних потреб
• інтеграція даних із зовнішніх джерел (API, файли, сторонні системи)
• забезпечення цілісності, консистентності та актуальності даних
• підготовка контрольних звітів та перевірок якості даних
• оптимізація продуктивності запитів та структури зберігання даних
• співпраця з аналітиками та BI-дизайнерами

Вимоги

• досвід розробки та супроводу баз даних
• впевнене володіння SQL
• розуміння принципів побудови сховищ даних (Data Warehouse)
• досвід реалізації ETL-процесів
• розуміння моделей даних (star schema, snowflake schema)
• досвід інтеграції даних з різних джерел
• уважність до якості даних та логіки обробки
• досвід роботи з PostgreSQL / MySQL / MS SQL / Oracle (або аналогами)
• досвід роботи з інструментами ETL (Airflow, dbt, Pentaho, Talend або подібні)
• базові знання Python або іншої мови для обробки даних
• досвід роботи з великими обсягами даних
• розуміння вимог аналітичної та управлінської звітності

Умови

• Повна зайнятість
• Робота в центрі м. Київ, офіс/гібрид.
• Супровід і підтримка команди професіоналів, що працюють над аналітикою та платформами даних.
• Можливість зробити свій професійний внесок у підвищення ефективності аналітичних процесів і прийняття рішень у державі.
• Можливість бронювання.

R&D Miltech компанія з активним масштабуванням у пошуку Computer Vision Engineer для роботи в Києві.

Розробляють рішення, які вже підтвердили свою надійність у бойових умовах - застосування вимірюються не десятками, а масовими серіями: сотні тисяч виробів у тиражі. Компанія закриває весь цикл: концепт, прототип, випробування, серія. Тому розробки команди доходять до реального виробництва, а не лишаються на рівні прототипу.

Ви будете займатись розробкою та вдосконаленням алгоритмів для задач донаведення з використанням тепловізійних камер.

2 швидкі етапи співбесід.
Формат роботи: офіс у Києві.
Гнучкий час початку робочого дня.

Пропонують: бронювання з перших днів (у тому числі з порушеннями обліку), медичне страхування, релокаційний пакет (у разі переїзду), робочу техніку, парковку, 24 календарні дні відпустки, оплачувані лікарняні та компенсацію навчання.

Необхідний досвід з Computer Vision, Object Detection & Tracking, розуміння принципів розгортання CV на Embedded пристроях.