Jobs Data Engineer

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.

About the work

We build and maintain industry intelligence databases - organizations, people, products and their relationships across sectors such as HealthTech, QuantumTech, LegalTech and wellness, covering multiple regions. Every one of them starts as messy public data and has to end up as a clean, deduplicated, verifiable dataset that analysts and downstream products can rely on.

That means the hard problems here are not "write a scraper". They are: how do you know two records are the same organization? How do you prove where a field came from six months later? How do you re-run a collection over a source that changed its markup, without corrupting what you already have?

You will work directly with the Head of Data Science and a team of AI and data engineers.

What you will do
 

• Build and own collection pipelines over public web sources - structured, semi-structured and awkward
• Design the enrichment layer: normalization, deduplication, entity resolution, confidence scoring
• Keep provenance on every field, so any value can be traced back to its source and date
• Use LLMs where they genuinely help - extraction from unstructured text, classification, taxonomy assignment - and know where they don't
• Take datasets from one-off collection to scheduled, monitored, re-runnable pipelines
• Define and enforce quality checks: coverage, freshness, duplicate rate, field completeness

What we're looking for

Both levels
 

• Strong Python - you write services and pipelines, not notebooks
• Real production scraping experience: sessions, rate limits, retries, blocks, anti-bot, JS-rendered pages, APIs that are not documented
• SQL and PostgreSQL beyond CRUD - indexing, query plans, schema design
• Practical experience with LLM APIs for extraction and classification, including how you validate their output
• Data quality instinct: you check what you collected before you hand it over

Additionally for the Senior opening
 

• You have owned a data platform, not just tasks on one - orchestration, scheduling, monitoring, backfills
• Experience with entity resolution or record linkage across sources that disagree with each other
• Airflow, Dagster or Prefect in production
• You have set the standards other people then worked to: schemas, conventions, review

Nice to have
 

• Docker and CI/CD
• Knowledge graphs, ontologies, or taxonomy design
• Vector search and RAG pipelines
• OSINT methodology and source verification
• Experience with data licensing or compliance constraints on public-web collection

How we hire
 

1. Intro call - 30 minutes, mutual fit and what you've actually built
2. Paid proof task - a real, bounded piece of our work. Paid at market rate, roughly 4 hours. We are not asking anyone to work for free
3. Technical review - we go through your solution together: your trade-offs, what you'd do with more time, what you'd do differently at ten times the volume
4. Offer - band determined by the level you land at, not by the title you applied under

We evaluate the proof task on judgment, not polish. Reproducibility, honest handling of edge cases, and clear reasoning about what you chose not to do count for more than a clever one-liner.
What we care about

Two things, mostly.

You question your own data. The engineers who work out well here are the ones who notice that a source silently changed its schema, that a "97% match rate" is hiding a systematic bias, that a field is populated but wrong. Volume without verification is worth nothing to us.

You explain your reasoning. Much of this work involves judgment calls that nobody can check quickly - which record wins a merge, what confidence threshold to use, when to stop enriching. We need those decisions written down and defensible, not buried in a script.

To apply: send your CV plus one short paragraph on the hardest data collection or deduplication problem you have solved, and what you got wrong on the way.

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

Overview

FutureMedia is building a centralized data platform for a portfolio of subscription-based digital products. We are looking for a hands-on Senior Data Engineer who can combine data engineering, platform thinking and strong delivery ownership. The role will help build Databricks on AWS, design reliable ingestion from private PostgreSQL estates and SaaS/API sources, standardize fragmented product and payments data, and deliver governed Bronze/Silver/Gold datasets for analysts and business stakeholders.
 

Why this role is interesting
 

• Greenfield platform: the engineer will shape standards, patterns and technical decisions rather than inherit a mature warehouse.
• Real platform challenge: distributed PostgreSQL databases run across private AWS/EKS environments and were built by different product teams.
• High-leverage first domain: the payments service is standardized across products and will be the first portfolio-wide model.
• Direct business impact: revenue, subscriptions, churn, LTV, CAC, product funnels and attribution.
• Close partnership with DevOps, Product/Backend teams, Data Analysts and business stakeholders.
   

Target platform

Infrastructure is managed through Terraform/IaC and Git-based review; Data Engineering code remains in the Data Platform repository.

What the Senior Data Engineer will own
 

• Build and operate Databricks on AWS together with the Data and DevOps teams.
• Develop reusable ingestion patterns from PostgreSQL, S3, APIs, webhooks and SaaS platforms.
• Implement incremental loads, historical backfills, idempotent reprocessing and schema-change handling.
• Design and maintain Bronze, Silver and Gold layers in Delta Lake.
• Unify payments data across products: users, subscriptions, transactions, refunds, chargebacks and renewals.
• Normalize product-specific schemas into common analytical entities and source-of-truth models.
• Build Databricks Jobs/Workflows, monitoring, freshness checks, reconciliation and data-quality controls.
• Contribute to Unity Catalog, groups, permissions, service principals and governance.
• Contribute infrastructure changes through Terraform and pull-request workflows.
• Optimize Databricks compute, SQL workloads and storage for reliability and cost.
• Work with Product/Backend teams to understand tables, relationships and business logic.
• Support analysts and stakeholders with trusted data marts for Payments, Marketing, Product, Finance and C-level reporting.
   

Candidate profile
 

Must have
 

• Strong production Data Engineering experience
• Advanced Python and SQL
• Hands-on Databricks + Spark/PySpark
• AWS experience (especially S3/IAM basics)
• PostgreSQL / relational data ingestion
• Incremental pipelines, backfills, idempotency
• Data modelling / medallion architecture
• Data quality, monitoring and troubleshooting
• Git and CI/CD
• Comfortable in a greenfield, ambiguous environment
   

Strong advantage
 

• Terraform / Infrastructure as Code
• Unity Catalog
• Databricks Workflows / Lakeflow
• AWS networking / VPC / EKS understanding
• CDC, AWS DMS or Debezium
• Subscription / payments data
• Stripe, Adyen, SolidGate or other PSPs
• Marketing / attribution data
• dbt
• Experience defining platform standards
  
   

First outcomes

• Platform foundation live: AWS + Databricks + S3 + Unity Catalog + Terraform/IaC.
• First end-to-end ingestion pattern proven and productionized.
• Payments domain onboarded and unified across products.
• Core Gold models available for revenue, subscriptions, churn and LTV.
• Repeatable product onboarding pattern documented.
• Monitoring, reconciliation, CI/CD and cost controls operating in production.

Project Description:

We are seeking an RPA Developer for our client to join a multidisciplinary team that includes frontend and backend developers, data engineers, data scientists, QA engineers, cloud engineers, and project managers. The ideal candidate is a strong problem solver who can work independently in a fast-paced, evolving environment. Experience with Power Automate Desktop and the broader Power Platform is highly desirable, along with familiarity with Blue Prism. A working knowledge of Python and SQL is required—while deep expertise is not necessary, these technologies should be well understood and not new to the candidate.

Responsibilities:

• Design, develop, and deploy RPA solutions using Power Automate and Blue Prism

• Implement and configure RPA software robots to execute tasks and streamline processes

• Collaborate with cross-functional teams to integrate RPA solutions with existing systems and applications.

• Troubleshoot and resolve issues related to RPA processes and bots in a timely manner.

• Conduct unit testing and validation of RPA solutions to ensure accuracy and reliability.

• Stay updated on the latest RPA trends, tools, and best practices.

Mandatory Skills Description:

Core must-have for this position (tech stack):

- Power Automate: Cloud flows and desktop flows. 2y+ experience with Power Automate Desktop. Ability to build complex workflow orchestration, integrate with APIs, databases, and enterprise systems, and handle long-running or stateful processes.

- Microsoft Power Platform: End-to-end understanding of the broader ecosystem.

- Blue Prism: Proven experience, particularly in the context of migration or transition initiatives.

- Programming background: Optional, but beneficial; examples include C#, Python, or similar languages.

Skills:

- Overall experience in a related field of more than 7 years

- Bachelor's degree in Computer Science or a related field.

- Environment strategy: Familiarity with Dev, Test, and Prod environment practices.

- Python programming skills

- Proficient with SQL

- Proficient with Python or C#

- Excellent problem-solving and troubleshooting skills.

- Effective communication and collaboration skills.

- Proactive attitude and a willingness to learn and adapt in a fast-paced environment.

Nice-to-Have Skills Description:

• Experience with performance testing and profiling

• Experience integrating with cloud infrastructure platforms, in particular Microsoft Azure

• Understanding of Financial Mathematics

Languages:

English: B2 Upper Intermediate

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*

Level: Middle 

Engagement: Long-term contract

Location: Europe (EU / EEA / UK), remote 

Working hours: EU business hours, shifted 2–3 hours later to ensure daily overlap with US West Coast (PST/PDT) mornings

About the 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 SDET to design and maintain automated tests for a large-scale distributed data platform. You will validate the correctness, performance, and reliability of the query engine, connectivity layer, and backend services across all major clouds.

This is a data-heavy, backend-focused role. We are not looking for web/UI QA engineers — the work centers on validating distributed query execution, data correctness at scale, connectivity drivers, and backend microservices.

Responsibilities

• Develop and maintain automated tests in Python / pytest for backend services, REST APIs, and distributed data components.
• Write end-to-end, integration, and regression tests covering SQL query execution, data correctness, and platform APIs.
• Support performance and load testing with JMeter, including workloads over JDBC / ODBC / Arrow Flight drivers.
• Validate data-intensive scenarios — query plans, result correctness across data sources, metadata consistency, and behavior under concurrency.
• Contribute to CI/CD test pipelines in Jenkins — configure, maintain, and troubleshoot.
• Provision and manage test environments in Kubernetes (GKE / EKS / AKS) across GCP, AWS, and Azure using Docker.
• Investigate failures across the stack — query engine, distributed services, drivers, infrastructure — and drive them to resolution with engineering.
• Collaborate with US-based developers on testability and quality gates.
•  

Required Qualifications

• Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field.
• Programming: Strong proficiency in Python, including pytest, with a solid grasp of OOP and software design principles.
• SQL & Data: Strong SQL skills and solid understanding of how relational and analytical data systems work.
• Data-intensive testing experience: Hands-on experience testing data-intensive systems — query engines, ETL/ELT pipelines, streaming platforms, or analytical databases. Candidates with only web/UI QA backgrounds are not a fit.
• Testing experience: 3+ years in backend/system test automation or SDET roles.
• CI/CD & DevOps basics: Practical experience with Jenkins pipelines and deploying/maintaining test environments.
• Containers: Working knowledge of Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency).
• Cloud: Comfortable operating in at least one major cloud (GCP, AWS, or Azure).
• Version Control: Confident with Git / GitHub workflows.
• English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team.
• Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team.
•  

Desired Skills

• Experience testing REST APIs and backend microservices at scale.
• Familiarity with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar).
• Understanding of modern data lakehouse concepts, open table formats (Apache Iceberg), and data warehousing.
• Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight.
• Performance testing with JMeter or comparable load-testing tools.
• Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure.
• IaC tools such as Terraform.
• Understanding of query plan generation, query acceleration / materializations, and metadata integrity in distributed data systems.

About the role

We're looking for a middle DevOps / SysOps engineer to take care of the infrastructure our product runs on: on-prem virtualization, network and VPN connectivity between sites, and the delivery pipelines on top of it. The team is small, so you get real ownership from the start — and room to grow into your own areas of responsibility.

Responsibilities

* Operate and develop on-prem virtualization: VMware ESXi + vCenter (clusters, resource pools, storage, patching and version upgrades)

* Administer network infrastructure: MikroTik, Cisco, pfSense — VLANs, routing, NAT, firewall policies

* Build and maintain WireGuard site-to-site and client VPN topologies between offices, labs and cloud

* Maintain Ubuntu servers: systemd, journald, configuration management, hardening, monitoring and alerting

* Support Docker-based build and deployment workflows

* Develop and maintain CI/CD pipelines in GitHub Actions

* Manage S3-compatible object storage for backups and build artifacts

* Own backup and restore procedures, including regular recovery testing

* Write technical documentation and runbooks for what you build

* Participate in incident response and on-call rotation

Requirements

* 2+ years of commercial experience in DevOps / system administration / infrastructure

* Production experience with VMware ESXi and vCenter

* Hands-on experience with at least two of: MikroTik RouterOS, Cisco, pfSense / OPNsense

* Networking fundamentals: L2/L3, VLAN, static and dynamic routing, NAT, firewall policies, DNS, DHCP, TLS, SSH

* WireGuard (or IPsec / OpenVPN): configuring tunnels and routing between sites, troubleshooting asymmetric routing

* Docker and Docker Compose: builds, registries, networking, volumes

* CI/CD with GitHub Actions (or GitLab CI with readiness to move to GHA)

* Linux (Ubuntu): confident CLI, systemd, journald, bash scripting

* Basic experience with S3-compatible storage or a public cloud provider (AWS / Azure / GCP / Hetzner)

* Monitoring and logging basics: Zabbix, Prometheus + Grafana, or ELK

* English for technical communication (reading docs, written async communication)

Nice to have

* Cisco FTD / FMC, Cisco ISE (802.1X, NAC, posture)

* Certifications: VCP-DCV, CCNA / CCNP Security, MTCNA / MTCRE / MTCINE, LFCS / RHCSA, AWS SAA

* Other VMware products: vSAN, NSX, Horizon (VDI), VCF, Site Recovery Manager, Veeam Backup & Replication

* Automation and IaC: Ansible, Terraform, Python

* Information security fundamentals: PKI, secrets management, hardening baselines (CIS), secure boot

* Bare-metal / Raspberry Pi / embedded Linux

* Experience with isolated or air-gapped environments

What we offer

* Competitive salary

* Reservation from mobilization

* Office in Kyiv with shelter access, generator and Starlink

* Possibility of hybrid work format

* Impact on improving our country's defense capabilities

* 21 days of vacation + public holidays, sick leaves

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).

Description

The Digital Health organization is technology team which focused on next generation Digital Health capabilities which deliver on the Medicine mission and vision to deliver Insight Driven Care. This role will operate within the Digital Health Applications & Interoperability subgroup of the broader Digital Health team, focused on patient engagement, care coordination, AI, healthcare analytics & interoperability amongst other advanced technologies which enhance our product portfolio with new services, while improving clinical & patient experiences.

The project is a cloud-based PaaS Ecosystem built with a privacy by design centric approach to provide a centralized cloud-based platform to store, classify, and control access to federated datasets in a scalable, secure, and efficient manner.

The ecosystem will allow Customer Operating Units (medical device departments) to store federated data sets of varying sizes and formats and control access to those data sets through Data steward(s). Source data sets can be exposed to qualified use cases and workflows through different project types.

The Healthcare Data Platform ecosystem will provide ML/AI project capabilities for streamlined development processes and a ML/AI workbench to enhance data exploration, wrangling, and model training.

In queue: 15+ OU’s. At this moment focused on – Nuero, Cardio, Diabetes is the OU that data platform is working with, but there could be more OU’s coming up with requirements in future.

GL Role: is to work on the enhancement of current capabilities, including taking over the work that AWS proserve team is doing, and develop new requirements that will keep coming from different OU’s in the future.

Requirements

• AWS Platform: Working experience with AWS data technologies, including S3, AWS RDS, and Lake Formation
• Programming Languages: Strong programming skills in Python
• Data Formats: Experience with JSON, XML, and other relevant data formats
• CI/CD Tools: Ability to deploy using established CI/CD pipelines using GitLab CI, Jenkins, Terraform or similar tools
• Scripting and automation: experience in scripting languages such as Python, PowerShell, etc.
• Monitoring and Logging: Familiarity with monitoring & logging tools like CloudWatch, Splunk, ELK, Dynatrace, Prometheus
• Source Code Management: Expertise with GitLab
• Documentation: Experience with markdown and in particula,r Antora for creating technical documentation
• Previous Healthcare or Medical Device experience
• Experience implementing enterprise grade cyber security & privacy by design into software products
• Experience working in Digital Health software
• Experience developing global applications
• Strong understanding of SDLC;
• Experience with Agile methodologies
• Software estimation
• Experience leading software development teams onshore and offshore

Job responsibilities

• Implement data pipelines using AWS services such as AWS Glue, Lambda, Kinesis, etc
• Implement integrations between the data platform and systems such as Atlan, Trino/Starburst, etc
• Complete logging and monitoring tasks through AWS and Splunk toolsets
• Develop and maintain ETL processes to ingest, clean, transform and store healthcare data from various sources
• Optimize data storage solutions using Amazon S3, AWS RDS, Lake Formation and other AWS technologies.
• Document, configure, and maintain systems specifications that conform to defined architecture standards, address business requirements, and processes in the cloud development & engineering.
• Participate in planning of system and development deployment as well as responsible for meeting compliance and security standards.
• Actively identify system functionality or performance deficiencies, execute changes to existing systems, and test functionality of the system to correct deficiencies and maintain more effective data handling, data integrity, conversion, input/output requirements, and storage.
• Document testing and maintenance of system updates, modifications, and configurations.
• Leverage platform process expertise to assess if existing standard platform functionality will solve a business problem or customization solution would be required.
• Test the quality of a product and its ability to perform a task or solve a problems.
• Perform basic maintenance and performance optimization procedures in each of the primary operating systems.
• Ensure system implementation compliance with global & local regulatory and security standards (i.e. HIPAA, SOCII, ISO27001, etc…)