Jobs Data Engineer

About client

Client builds and operates GPU cloud infrastructure at the scale modern AI demands. We run dense accelerated-compute clusters on bare metal, powered by NVIDIA and AMD accelerators and K8s, stitched together with high-performance Ethernet and InfiniBand fabrics, and we deliver them to customers as reliable, secure, high-throughput platforms. We are scaling rapidly across new data center sites, and the infrastructure team sits at the center of that growth, building the systems that turn racks of hardware into a product customers depend on.

The role

We are hiring a Principal Infrastructure Engineer to build the foundation of our greenfield v2 cloud platform: the bare-metal provisioning and control-plane substrate that turn new racks of GPU hardware into reliable, secure, customer-ready capacity.This is a build-focused, hands-on role and the most senior individual contributor on the platform build. You will own the recommendation and the call on the infrastructure layer (we’re currently in POC across different vendors) and the build-vs-buy decision that goes with it, with our Cloud Platform Architect pressure-testing it. You will drive integration across the other domains and define how infrastructure is templated, multi-tenanted, and handed to customers. You will define the architecture and the standards the rest of the team builds to, while still being deep in the code yourself. Day-to-day operations are not your focus, getting the platform designed and built right is.

You will work at the center of new cluster and new-site buildouts, partnering closely with our network, SRE, and security engineers. They own the fabric and day-to-day operations; you own getting the platform built right. Because the systems you design become the foundation customer GPU workloads run on, getting the architecture and automation right the first time matters more here than almost anywhere.

What you’ll own

• Platform architecture and technical direction: own the architecture of the cloud platform control plane and orchestration layer for new GPU clusters, making the foundational decisions and setting the technical standards the rest of the platform is built on.

• Bare-metal provisioning automation: build the automated pipeline that takes a node from rack-and-stack handoff to a fully configured, real acceptance gates before a node is marked customer-ready, firmware to the qualified recipe, GPU burn-in, NCCL/RCC runs, and fabric checks, plus a golden-image build-and-qualify pipeline in CI repeatedly across sites and at scale.

• Infrastructure as code and automation: treat the platform as code from day one. Build the Terraform and Ansible that provision, configure, and validate the platform, so the build is reproducible rather than hand-assembled.

• Control-plane substrate: build the control plane that turns provisioned bare metal into cluster-ready capacity, and own the clean handoff to the Kubernetes layer. You own tenant isolation at the network and hardware layer; the Kubernetes and namespace isolation model belongs to the Principal Kubernetes Platform Engineer.

• DPU and SmartNIC integration: design how we leverage BlueField DPUs in the platform, offload, provisioning, isolation, and the SuperNIC/DPU-mode configurations that let us get the most out of the hardware.

• Platform reliability by design. Build observability, guardrails, and progressive-rollout mechanisms into the platform from the start. As you build it and design to our SOC 2, ISO 27001, and HIPAA obligation.

• Security Built-in: instrument the platform as you build it and design to our SOC 2, ISO 27001, and HIPAA obligations. Vault-based secrets model from the start. BIOS and system prep for trusted domain execution, secure boot, and backend-fabric hardening.

• Confidential-compute-ready platform (growth area): Design the tenancy and scheduling so it fits our confidential-compute roadmap: whole-GPU per tenant, and TDX with GPU passthrough. Deep CC background is a plus, not a requirement.

• New site buildouts: take a critical role in standing up the platform at new data center sites, turning green-field facilities into production capacity.

• Technical leadership: set patterns, review designs, and raise the engineering bar across the infrastructure team multiplying the impact of other engineers and mentoring senior ICs, without becoming a people manager.

What we’re looking for

Required

• 10+ years building production infrastructure at scale, including deep green-field and platform-build experience, for cloud, hosting, or high-performance compute environments.

• A track record of setting technical direction on complex platforms, architecture others adopt, standards others follow, and influence that extends well beyond your own keyboard.

• Strong Linux systems fundamentals, you can reason about the kernel, networking stack, storage, and performance, not just the command line.

• Fluency in infrastructure as code and automation: Terraform, Ansible, and a real programming language used to build durable platform tooling, not just one-off scripts.

• Production Kubernetes experience: building, deploying, and operating clusters and the platform services that run on them.

• A demonstrated bias toward building things right the first time: sound architecture, reproducible automation, and durable design over short-term shortcuts.

• Strong written communication, you document designs and decisions clearly and leave systems more understandable than you found them.

Strongly preferred

• DPU / SmartNIC experience, especially leveraging offload, DPU/SuperNIC modes, and platform designs that exploit them. This is a meaningful differentiator for us.

• Experience with GPU and accelerated-compute infrastructure across NVIDIA and AMD, including ROCm and firmware lifecycle, or HPC/AI infrastructure.

• Bare-metal provisioning at scale (Redfish/BMC automation, NetBox or a similar source of truth).

• Experience standing up cloud-platform control-plane stacks in a green-field setting.

• Networking depth, BGP, VLANs, fabric concepts, and the ability to debug connectivity and throughput issues. You won’t own the network, but the more fluently you can partner with our network engineers, the better.

• Familiarity with high-performance fabrics: RoCE Ethernet / InfiniBand.

• HashiCorp Vault, and building within a SOC 2 / ISO 27001 / HIPAA compliance posture.

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 white-glove standard that 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.

You don’t need to be an AI expert, but you should be eager to fold these tools into how you design, build, and automate — and to help us push the frontier of what an AI-leveraged infrastructure team can do.

What success looks like

First 30 days

• Ramped on our environment: the platform stack, provisioning approach, and target architecture. able to navigate the codebase and design docs independently.

• Shipped your first meaningful contribution to the platform build with the team’s review.

• Aligned with network, SRE, and security on where your work meets theirs.

First 90 days

• Owning a defined component of the platform build end to end, from design through delivery.

• Delivered a meaningful piece of provisioning or platform automation that made the build faster, more reproducible, or more reliable.

• Shaped at least one foundational architecture decision that the platform is built on.

First 12 months

• The recognized technical authority for the cloud platform build, setting direction and trusted across network, SRE, security, and platform engineering.

• Designed and delivered a significant piece of the cloud platform or its automation that raised the team’s leverage and the platform’s reliability.

• Took a lead role in bringing at least one new data center site online.

About the Role

We are looking for an experienced Senior Data Engineer to design, build, and maintain scalable data platforms and pipelines. You will work closely with data analysts, data scientists, software engineers, and business stakeholders to deliver reliable, high-quality data solutions that power analytics and business decision-making.

This role is ideal for someone passionate about building modern data architectures, optimizing data workflows, and ensuring operational excellence.

Responsibilities

• Design, develop, and maintain scalable batch and streaming data pipelines.
• Build robust ETL/ELT workflows for ingesting, transforming, and serving data.
• Optimize data storage, processing, and query performance.
• Develop and maintain data models that support reporting, analytics, and machine learning.
• Ensure data quality, reliability, security, and governance across the platform.
• Monitor production pipelines and troubleshoot operational issues while maintaining SLAs.
• Collaborate with cross-functional teams to translate business requirements into technical solutions.
• Improve automation, deployment processes, and infrastructure for data engineering workloads.
• Contribute to architectural decisions and engineering best practices.
• Document technical solutions and mentor junior engineers when needed.

Required Skills

• 5+ years of experience in Data Engineering or a similar role.
• Strong proficiency in Python and SQL.
• Experience building scalable ETL/ELT pipelines.
• Hands-on experience with modern cloud platforms (AWS, Azure, or Google Cloud).
• Experience working with data warehouses such as Snowflake, BigQuery, Redshift, or Synapse.
• Strong understanding of data modeling principles.
• Experience with orchestration tools such as Airflow or similar.
• Knowledge of distributed data processing technologies (Spark preferred).
• Experience with Git and software engineering best practices.
• Strong analytical and problem-solving skills.
• Experience supporting production systems, troubleshooting incidents, and maintaining operational reliability and SLAs.
• Excellent communication skills with both technical and non-technical stakeholders.

Nice to Have

• Experience with dbt for data transformation and modeling.
• Knowledge of data observability and monitoring tools.
• Experience with streaming technologies such as Kafka, Apache Flink, or similar.
• Familiarity with CI/CD pipelines for data engineering.
• Experience implementing data quality frameworks and governance practices.
• Experience with Infrastructure as Code (Terraform or similar).
• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience).

What We Offer

• Fully remote work ( shifts per USA time zone)
• Opportunity to work with modern cloud-based data platforms.
• Long-term international project.
• Collaborative engineering culture.
• Competitive compensation based on experience.
• Career growth opportunities and exposure to cutting-edge data technologies.

We are looking for a Senior Data Engineer. This is not a standard ETL role. You will work on a high-load system processing 10,000+ executions per minute, building scalable pipelines where AI and LLM logic are embedded directly into production data flows.

The role combines data engineering, backend thinking, infrastructure ownership, and real production AI integration.

What you will do:

• Design and maintain scalable data pipelines in Python on Google Cloud Platform
• Build and optimize AI-driven scoring flows using LLMs for real-time identity enrichment
• Work with BigQuery, Apache Spark, and Apache Iceberg for heavy data processing
• Maintain high-load orchestration with custom Python nodes and complex workflows
• Own deployment and release processes (CI/CD)
• Optimize PostgreSQL performance under heavy load
• Investigate incidents and resolve root causes permanently

• Use AI engineering tools such as Claude in daily development

   

Requirements:

• 4+ years of experience in Data Engineering / Backend Engineering
• Strong Python skills
• Commercial experience with production LLM integration (OpenAI / Anthropic / Gemini)
• Strong knowledge of GCP (Cloud Functions, IAM, Networking)
• Experience with BigQuery, Spark, Iceberg
• Good understanding of Kubernetes
• Experience with workflow orchestration tools (n8n is a strong plus)
• Strong practical knowledge of PostgreSQL

• English level B2+

   

What is important for us:

• Ownership mindset
• Fast learning ability
• Strong problem-solving approach
• Active AI usage in engineering work
• Focus on practical results over unnecessary complexity

Intempt runs 36+ microservices on AWS EKS (Kubernetes 1.25) processing 350M+ events/month. Aurora PostgreSQL 13.8, MSK Kafka 3.3.1, ClickHouse, Apache Flink, HashiCorp Vault, and Redis are all managed through Terraform with SOPS-encrypted secrets, deployed via GitHub Actions CI/CD with OIDC auth.

The infrastructure works. We need someone to own it.

You'll manage the full lifecycle: EKS clusters with spot instances across node groups, Aurora provisioned instances with logical replication, MSK brokers with TLS and auto-scaling storage, Vault HA with KMS auto-unseal, cert-manager for Route53 DNS validation, ALB ingress controllers, cluster autoscaler, EFS, Noobaa object storage, and S3+CloudFront static site delivery. Every service has its own ECR repo with immutable tags, scan-on-push, and a dedicated GitHub Actions deployer role scoped to exactly the permissions it needs.

What you'll own

• EKS cluster operations — node group scaling, spot instance management, Fargate profiles, security groups, OIDC provider config
• Terraform IaC across all environments (staging/QA/production) with S3 state backend and SOPS+KMS secret management
• Database reliability — Aurora PostgreSQL 13.8 (provisioned, 2 instances, encrypted, logical replication), ElastiCache Redis, ClickHouse on Altinity
• Streaming infrastructure — MSK Kafka 3.3.1 (TLS, CloudWatch logs, Prometheus JMX/Node exporters, auto-scaling storage) and Flink Kubernetes Operator 1.4.0
• CI/CD pipeline — GitHub Actions with OIDC auth, 20+ per-service deployer roles mapping git repos to ECR repos, plan/apply workflow with manual production gates
• Secrets and service identity — HashiCorp Vault HA on EKS with PostgreSQL backend, KMS auto-unseal, Transit engine, AppRole auth
• Observability — DataDog agents, Prometheus exporters, CloudWatch, incident response and runbooks
• Backup and DR — AWS Backup (daily, 30-day retention) covering S3 and EFS, RDS automated backups, point-in-time recovery

You are

• 3+ years running production Kubernetes on AWS — not just deploying to it, actually operating clusters at scale
• Deep Terraform experience — you've managed multi-environment IaC with remote state, modules, and encrypted secrets
• Comfortable with managed Kafka (MSK or Confluent), stream processing (Flink/Spark), and the failure modes of distributed messaging
• Familiar with HashiCorp Vault operations — unsealing, secret rotation, Transit engine, AppRole patterns
• Someone who has set up and maintained CI/CD pipelines with least-privilege IAM roles and OIDC federation
• Strong in networking fundamentals — VPCs, security groups, NLBs, ALBs, DNS (Route53), TLS certificates (ACM/cert-manager)
• The person who automates themselves out of toil and writes the runbook before the pager goes off

Stack you'll live in

AWS (EKS, Aurora, MSK, ElastiCache, S3, CloudFront, Route53, ACM, EFS) · Kubernetes 1.25 · Terraform · GitHub Actions · HashiCorp Vault · Apache Flink · ClickHouse (Altinity Cloud) · Loki · Helm · SOPS + KMS · Docker / ECR · Prometheus

Not a fit if

• You've only used managed Kubernetes through a UI — we need someone who reads the Terraform and knows what every resource does
• You think infrastructure-as-code means a CloudFormation template someone else wrote
• You can't debug a pod that won't schedule without googling every error message

Key Responsibilities

• Design, develop, and optimize deep learning models using PyTorch.
• Build and maintain scalable training and evaluation pipelines.
• Lead model selection, experimentation, and fine-tuning activities.
• Improve training performance, efficiency, and scalability across large datasets and compute environments.
• Work with multimodal data sources, including video, images, and audio.
• Provide technical guidance on AI architecture, training strategies, and best practices.
• Collaborate closely with product, engineering, and research teams to translate business objectives into AI solutions.
• Support deployment and operationalization of machine learning models in production environments.

Required Experience

• 5+ years of experience in Machine Learning, Data Science, or AI Engineering roles.
• Strong hands-on experience developing deep learning models with PyTorch.
• Proven experience building and optimizing training pipelines for machine learning systems.
• Deep understanding of model training, hyperparameter tuning, and performance optimization.
• Experience working with computer vision, audio processing, or multimodal AI applications.
• Strong software engineering skills and experience developing production-ready ML solutions.
• Experience with cloud-based or distributed training environments.

Preferred Qualifications

• Experience applying machine learning techniques within behavioral science, human behavior analysis, affective computing, or related domains.
• Experience with video understanding, speech/audio analysis, or multimodal foundation models.
• Familiarity with MLOps practices, model monitoring, and scalable deployment architectures.
• Experience working with large-scale datasets and distributed training frameworks.

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!

The company is a healthcare data analytics provider offering modern data infrastructure, reporting, and AI‑driven insights. Their technology focuses on improving how healthcare organizations manage, analyze, and use clinical data.

Core Solutions

•  Healthcare data platform: Consolidates structured and unstructured patient data in near real time to create a unified data foundation.

•  Longitudinal clinical data ecosystem: Enables comprehensive management and analysis of patient information.

•  Patient engagement platform: Integrates data from multiple sources to support secure communication and holistic patient care.

•  Population health management suite: Aggregates clinical and claims data, identifies at‑risk groups, and supports interventions to improve outcomes and reduce costs.

•  Care coordination and analytics tools: Provide a complete view of patient data to enhance quality, reduce costs, and support risk stratification.

•  AI and analytics services: Transform healthcare data into actionable insights for better care and operational efficiency.

Main expectations:
1.  Strong Python 3.x backend development (Flask + multithreading)
2.  Solid SQL skills and hands‑on experience with PostgreSQL
3.  Practical experience with BigQuery and Google Cloud services
4.  Containerization skills (Docker/Podman) and basic Kubernetes knowledge
5.  ETL/ELT pipeline experience and large‑scale data processing

Conditions:

• Compensation: 5500–6500 USD/month

• Contract: B2B

• Remote‑friendly

Vacancy Description

We are building a broad blockchain ecosystem – an L2 network, wallet infrastructure, multiple stablecoin products, and the supporting services around them – and we are looking for a DevOps Engineer to help us run and scale the platform behind it. The work is hands-on across platform engineering, reliability, and operations: high-availability services, on-chain integrations, secure production environments, and infrastructure that grows with the products it supports.

Requirements

• 4+ years of professional DevOps or infrastructure experience, including leadership or mentorship roles.
• Deep expertise in Azure cloud provider and familiarity with others.
• Strong hands-on experience with Terraform.
• Proven production-grade Kubernetes experience: cluster provisioning (AKS preferred), Helm charts, Operators.
• Design, implementation, and maintenance of CI/CD pipelines using GitLab or GitHub CI, Argo CD, or similar tools.
• Proficiency in scripting languages for automation and custom tooling.
• Knowledge of secrets management (HashiCorp Vault, Azure Key Vault).
• Solid understanding of TCP/IP, DNS, load balancing, CDN integration, and network security concepts.
• Experience designing storage strategies backup, restore, and Disaster Recovery planning.
• Ability to intervene in operational matters and assist the team directly when necessary.
• Excellent communication and collaboration skills with cross-functional stakeholders.
• High attention to detail, reliability, and ability to meet deadlines in a fast-paced environment.
• Capability to identify risks early, communicate options clearly, and drive timely mitigation.
• Be on time, prepared, and reliable for meetings, handoffs, and releases.

• English skills at least Upper-intermediate.

   

Would be a plus

• Experience with enclaves and secure environments (SGX, Nitro Enclaves or similar)
• Strong consultative skills: ability to translate client business goals into technical requirements and actionable roadmaps.
• Comfort working with multiple concurrent client projects, balancing competing priorities, and triaging incidents.
• Exceptional verbal and written communication: present technical solutions, write clear documentation, and lead regular status meetings with both technical and non-technical stakeholders.
• Passion for upskilling team members: conduct workshops on new tools (e.g., migrating on-premise workloads to Kubernetes) or share best practices for secure coding and infrastructure hardening.
• Understanding of more complex blockchain-related terms and concepts (rollups, smart-contracts, consensus, etc.)

• Hands-on experience with Web3/Blockchain projects and related infrastructure

   

Responsibilities

• Continuously evaluate new tools, platforms, and best practices to improve delivery speed, reliability, and security
• Participate in the planning and implementation of new approaches and tools
• Architect and implement CI/CD pipelines for diverse technology stacks (microservices, monoliths, serverless).
• Develop and maintain Infrastructure-as-Code (IaC) modules (Terraform) for reproducible environments.
• Oversee container orchestration (Kubernetes) and related tooling (Helm, Argo CD).
• Define and enforce network, storage, and compute configurations to meet scalability and high-availability requirements.
• Evaluate, select, and integrate monitoring, logging, and alerting solutions (Prometheus, Grafana, ELK).
• Propose best practices for peer reviews, knowledge sharing, and continuous learning.
• Support multiple project teams in an ad-hoc manner

Client

Our client is a large international manufacturing company listed in the S&P 500, operating a complex, multi-site global environment. As part of a Data Strategy Development initiative, the organization is defining a clear, pragmatic, and scalable data strategy to improve decision-making, reduce manual processes, strengthen data ownership, and establish a strong foundation for future analytics and digital transformation. The engagement focuses on strategy definition and is expected to continue into platform and analytics implementation phases.

Project overview

This initiative focuses on establishing a robust, enterprise-wide data strategy and building a data-driven culture. The project involves standing up a modern target operating model, deploying advanced data platforms, and creating the data leadership architecture necessary to support long-term business capabilities.

Position overview

The Data Consultant will lead the people and process aspects of the organization's data strategy, specifically driving data governance, change management, and the data literacy agenda. Serving as a business-facing leader, this role acts as the primary bridge between business stakeholders, executive sponsors, and the technical delivery teams. The ideal candidate possesses strong business acumen paired with sufficient technical literacy to maintain credibility with data architecture teams and ensure seamless strategic alignment.

Responsibilities

• Data Governance: Lead the implementation of the data governance blueprint, including the operating model, defining roles (data owners and stewards), establishing policies and standards, and facilitating and sustaining governance forums.
• Change Management: Own and execute the comprehensive change management plan, covering stakeholder engagement, communications, user adoption, and the transition to the target operating model.
• Data Leadership: Drive the data leadership and data literacy agenda to foster a mature, data-driven organizational culture and build internal capabilities.
• Stakeholder Partnership: Collaborate directly with business and functional leaders to capture requirements, prioritize data use cases, and secure organizational buy-in.
• Executive Alignment: Maintain alignment across executive sponsors, the Data Architect, and change program leads regarding project scope, priorities, risks, and milestones.
• Technical Alignment: Ensure all governance processes and adoption strategies are technically grounded by maintaining a strong baseline understanding of the deployed data platforms and architecture.
• Artifact Ownership: Author and quality-assure core strategic artifacts, including governance blueprints, operating models, and change plans tailored for executive audiences.

Requirements

• Proven track record of leading data governance and/or data management programs within a complex enterprise environment.
• Strong change management capability, with demonstrated experience in stakeholder engagement, communication strategy, and operating-model transitions.
• Proven ability to effectively engage, collaborate with, and influence senior and C-level stakeholders.
• Sound technical literacy regarding data platforms and analytics, enabling close partnership with architects and engineers.
• Exceptional communication and facilitation skills, with a verified ability to translate complex concepts between business and technical audiences.

Nice to have

• Experience in manufacturing or industrial enterprise environments.
• Background in large-scale data or digital transformation programs.
• Familiarity with Azure, Snowflake, and modern cloud data platforms.
• Experience contributing to executive-level strategy reports and board-level materials.
• Consulting or principal-level advisory experience in global organizations.

Our client is a prominent global wealth and asset management firm focused on delivering innovative investment solutions and a networked ecosystem of international opportunities. The organization operates within a highly regulated environment, prioritizing the transformation of complex concepts into actionable financial strategies for a global clientele.

Project overview

The project involves the development and expansion of a centralized, scalable data ecosystem. Having successfully ingested the initial data source, the team is now moving into a high-growth phase to integrate multiple high-value financial and operational data streams. The goal is to provide C-level executives and advisors with a unified, reliable reporting layer for strategic decision-making.

Position overview

As a Senior Data Engineer, you will be a technical cornerstone of the data platform team. You will be responsible for the end-to-end engineering of data pipelines, from ingestion to transformation, ensuring that the Snowflake warehouse is optimized for complex wealth management analytics. You will work at the intersection of sophisticated financial data and enterprise-grade engineering.

Responsibilities

• Build and maintain scalable data pipelines for ingestion into Snowflake.
• Develop and manage transformations using dbt, including modeling, testing, and comprehensive documentation.
• Design and orchestrate complex data workflows using Prefect.
• Utilize Workato to integrate and normalize data from critical SaaS platforms, including Addepar, NetSuite, Salesforce, Morningstar, and UKG.
• Design specialized data models tailored for portfolio, client, and financial reporting.
• Ensure data quality, lineage, and governance best practices while optimizing performance and costs within Snowflake.
• Partner with Tableau developers and business stakeholders to align technical delivery with reporting needs.

Requirements

• 5+ years of experience in data engineering with a focus on building modern data platforms.
• Hands-on experience with Snowflake and dbt, supported by strong SQL skills and advanced data modeling expertise.
• DBT certification.
• Proven familiarity with ETL/ELT patterns and API-based ingestion.
• Practical experience with orchestration tools such as Prefect or Airflow.
• A solid understanding of financial or wealth management data structures.

Nice to have

• Experience supporting reporting requirements for Tableau-based visualization layers.
• Experience within the Wealth Management or Investment Management sector, with specific familiarity handling investment and portfolio-related data.
• Understanding of regulatory requirements regarding financial data handling and privacy.
• Background in integrating market data providers such as Bloomberg or Morningstar.
• Advanced SQL skills and experience with Python for data engineering tasks.

About the Role

We are looking for a Senior Data Engineer with hands-on Palantir Foundry experience to design, build, and operate scalable data platforms supporting analytics, operations, and data-driven decision-making. You will work on complex, production-grade data systems and collaborate closely with product, analytics, and engineering teams.

Responsibilities

• Design, build, and maintain end-to-end data pipelines using Palantir Foundry
• Develop and manage data models, transformations, and workflows within Foundry
• Integrate data from multiple sources (APIs, databases, files, event streams)
• Ensure data quality, reliability, security, and governance across data products
• Optimize performance and scalability of data pipelines and analytics workloads
• Collaborate with product managers, analysts, and stakeholders to translate business needs into data solutions
• Support analytics, reporting, and advanced use cases (including ML/AI where applicable)
• Contribute to best practices, documentation, and data platform standards

Requirements

• 5+ years of experience as a Data Engineer (senior level)
• Strong hands-on experience with Palantir Foundry in production environments
• Excellent proficiency in Python and SQL
• Experience building and operating cloud-based data platforms
• Solid understanding of data modeling, ETL/ELT, and data warehousing concepts
• Strong focus on data quality, governance, and security
• Experience working in Agile, cross-functional teams
• Must be located in Europe

Nice to Have

• Palantir Foundry certification
• Experience with AWS, Azure, or GCP
• Exposure to analytics engineering or data product development
• Experience in regulated or enterprise environments
• Familiarity with CI/CD and infrastructure-as-code practices

Employment type: Freelance / Project-based
Duration: 6+ months
Location: Czech Republic, Hungary, Slovakia (must be based in Europe)
Language: English (professional working proficiency required)

🚀 We are looking for a Middle+/Senior Data Engineer to join a long-term data platform project.

 

The role is focused on designing, building, and maintaining reliable data pipelines and cloud-based data platforms for high-load business environments. The ideal candidate has strong hands-on experience with Python, SQL, ETL/ELT processes, and AWS data services.

🎯 Responsibilities

 

- Design, build, and maintain scalable batch and near-real-time data pipelines.

- Develop ETL/ELT workflows using Python, SQL, and modern data engineering tools.

- Integrate data from APIs, databases, files, SaaS platforms, and event-based sources.

- Build and support data lakes, data warehouses, and analytics-ready datasets.

- Improve pipeline performance, reliability, scalability, and cost efficiency.

- Implement data quality checks, monitoring, logging, validation, and alerting.

- Troubleshoot production data issues and identify root causes.

- Collaborate with data analysts, BI engineers, data scientists, software engineers, and business stakeholders.

- Maintain technical documentation, data models, and pipeline specifications.

- Contribute to CI/CD, infrastructure automation, and cloud platform improvements.

 

🧩 Requirements

 

- 3+ years of commercial experience in Data Engineering or a closely related role.

- Strong hands-on experience with Python.

- Strong SQL skills, including complex transformations and query optimisation.

- Practical experience building and maintaining production-grade ETL/ELT pipelines.- Experience working with AWS data services, particularly S3 and at least one of the following:

  - AWS Glue;

  - Athena;

  - EMR;

  - Redshift;

  - Lambda.

- Experience with at least one orchestration or transformation tool such as Airflow, dbt, Prefect, Dagster, or similar.

- Experience working with at least one cloud data warehouse or data platform such as Snowflake, Databricks, BigQuery, or Redshift.

- Strong understanding of data warehousing, data lakes, data modelling, partitioning, and file formats.

- Experience implementing data quality checks, monitoring, and error handling.

- Experience with Git and CI/CD workflows.

- Ability to independently investigate issues, make technical decisions, and take ownership of delivery.

- English level: Upper-Intermediate or higher.

 

🧩 Nice to Have

 

- Experience with Spark or PySpark.

- Experience with Kafka, Kinesis, Pub/Sub, or other streaming technologies.

- Experience with Delta Lake, Apache Iceberg, or Apache Hudi.

- Experience with Terraform, AWS CDK, or another Infrastructure as Code tool.

- Experience with Docker or Kubernetes.

- Experience with high-load or analytics-heavy products.

- Experience with GCP or Azure data services.

- Experience supporting AI/ML or LLM-related data pipelines.

🧩You'll have an opportunity to:

Build scalable data platforms using modern cloud technologies.

Work with large datasets and production-grade data pipelines.

Collaborate with experienced international engineering teams.Solve complex data engineering challenges across different industries.

Continuously develop your technical expertise with modern data tools and cloud services.

Take ownership of features and contribute to high-quality engineering practices.

What You’ll Own

• Build production systems for AI-driven vulnerability research and exploit development.
• Develop tooling for vulnerability discovery, triage, reproduction, and validation at scale.
• Work across OS internals, binaries, runtimes, kernels, dynamic analysis, instrumentation, and low-level performance.
• Partner with vulnerability researchers, security operators, and engineers to automate manual cyber workflows.
• Own problems end-to-end, from research and design to shipped production code.

Requirements

• 2+ years of vulnerability research, offensive security, or low-level engineering experience. Exceptional new grads with strong CTF, systems, or exploitation work will be considered.
• Hands-on experience with reverse engineering, binary analysis, exploit development, fuzzing, dynamic analysis, bug hunting, CTFs, or comparable low-level security work.
• Deep low-level systems fluency, including OS internals, kernels, runtimes, memory corruption, debugging, dynamic instrumentation, or performance-sensitive systems.
• Strong builder mindset and ability to ship reliable tools and production systems.
• Comfortable working in ambiguity and moving fast.
• Willing to relocate to Washington DC after an initial period, with full relocation support. The role is expected to be in-office 5 days per week.

Nice to Have

• Strong CTF background, especially pwn, rev, kernel, browser, or systems-heavy challenges.
• Published vulnerability research, CVEs, exploit writeups, or security talks.
• Experience with fuzzing, symbolic execution, emulation, program analysis, or binary rewriting.
• Open-source contributions to low-level security, reversing, debugging, tracing, fuzzing, or OS tooling.
• Exposure to ML / AI systems, especially applied to code, binaries, security automation, or program analysis.
• Early-stage startup, founder, or early-engineer experience.

What We Look For

• Strong vulnerability research instincts.
• Low-level technical depth.
• Builder mindset — able to turn research workflows into scalable systems.
• Comfort with ambiguity, speed, and high ownership.
• Motivation to help build a category-defining company in AI-native offensive cyber.

Must-have skills

• Ad-monetization domain (25%) — deep understanding of RTB, Header Bidding, Waterfalls, Fill Rate, eCPM/CPM optimization, and advertiser demand.
• Ad mediation platforms + mobile advertising SDKs (20%) — hands-on integration and optimization.
• Software development (20%) — strong backend, distributed systems, APIs, and performance optimization.
• Mobile development (15%) — Android and/or iOS.
• Analytical, data-driven optimization (12%) — analyze large-scale monetization data and implement data-driven improvements.
• High-scale consumer app experience (8%) — experience with high-scale consumer applications reaching millions of users.

Nice-to-have skills

• Hands-on with AppLovin MAX, Google Ad Manager, AdMob, Unity Ads, or IronSource.
• Building internal optimization engines using AI or Machine Learning.
• Familiarity with attribution, user segmentation, A/B testing, and predictive monetization models.

Responsibilities

• Design, develop, and optimize the core monetization infrastructure across multiple mobile applications.
• Improve ad waterfall and bidding performance to maximize eCPM, fill rate, ARPDAU, and total ad revenue.
• Integrate and optimize mediation platforms (AppLovin MAX, AdMob, LevelPlay, etc.).
• Build intelligent optimization systems for ad placement, frequency, segmentation, and pricing.
• Develop backend services and automation tools for real-time monetization decisions.
• Analyze monetization performance using large-scale data and implement data-driven improvements.
• Optimize SDK integrations, latency, memory usage, and overall ad performance.
• Work closely with Growth, Product, and Data teams to continuously increase LTV and user monetization.
• Research and implement new monetization technologies, bidding strategies, and demand sources.

What you'll own

• Increase overall monetization performance across millions of users.
• Maximize fill rate, eCPM, advertiser competition, and total revenue.
• Build the next generation of the monetization engine using automation and AI.
• Deliver measurable business impact through engineering excellence.

Українська miltech-компанія, що створює технології, які щодня працюють на фронті-шукаємо досвідченого CV Engineer.

Чому ця роль унікальна:

Результати вашої роботи:

• використовуються безпосередньо на фронті;
• впливають на результат бойових місій;
• допомагають зберігати інфраструктуру та життя людей;
• підсилюють обороноздатність України.

Ви наш кандидат, якщо:

• маєте 2+ роки досвіду в Computer Vision;
• маєте математичну або інженерну освіту;
• впевнено володієте С++ та Python;
• базовий стек: Досвід PyTorch / TensorFlow / досвід розробки на Linux;
• маєте базове розуміння автопілотів (PX4 / ArduPilot), протоколів керування (MAVLink) та наявної імплементованих рішень (Kalman Filter, EKF);
• маєте досід роботи з edge AI compute (Raspberry / Jetson / Coral TPU / інші);
• маєте відмінні problem solving навички

Бажано мати навички:

• visual Odometry / SLAM (ORB-SLAM, OpenVINS тощо) для дослідження та впровадження нових підходів;
• sensor fusion: IMU / камери / GPS та інші;
• досвід розробки CUDA kernels

Вашими завданнями будуть:

• інтеграція CV/ML алгоритмів під БПЛА-контекст. Дослідницька робота, здатність знаходити та аналізувати нові технічні матеріали;
• розробка production-ready коду на C++ / Python, feature ownership;
• повний цикл роботи з моделями: збір та підготовка даних, тренування та валідація, оптимізація під edge (latency / FPS);
• оптимізація та деплой моделей на onboard-системи (TensorRT, ONNX, квантизація, прунінг);
• участь у польових тестах, швидка ітерація алгоритмів

Ми пропонуємо

• бронювання протягом першого тижня роботи;
• допомогу з вирішенням питань військового обліку;
• конкурентну заробітну плату (розглядаємо очікування кандидата);
• офіційне працевлаштування;
• гібридний формат роботи (перший місяць для знайомства в офісі);
• графік: пн—пт, 10:00–19:00;
• можливі робочі суботи, які оплачуються додатково;
• виїзди на тести;
• медичне страхування;
• винагорода за реалізовані проекти;
• можливість працювати над технологіями, які реально використовуються на фронті;
• наявність укриття.