Jobs Data Engineer

Start: ASAP
Long-term project
Candidate location: EU !!!
Location: Berlin (Hybrid) or fully remote
Department: Technology / Digital Transformation

You'll own the infrastructure, automation, and delivery pipelines that power our internal platforms and integrations — working across cloud (Azure/GCP), containerized workloads, and enterprise systems (SAP, NestJS-based services, and internal tooling). This is a senior, hands-on role with real architectural ownership: you'll shape how we build, secure, and scale our platform, not just maintain what already exists.

What You'll Do
• Own CI/CD and delivery pipelines end-to-end — design, harden, and continuously improve pipelines with built-in security gates (SAST/DAST, dependency and container scanning, IaC compliance checks) rather than bolting security on after the fact.
• Build and evolve infrastructure as code (Terraform, and/or Pulumi) across cloud environments, treating Git as the single source of truth (GitOps) with full auditability and rollback.
• Design and operate Kubernetes-based container platforms — clusters, Helm charts, and where relevant, service mesh (Istio/Linkerd) for multi-service, multi-cluster environments.
• Drive platform engineering initiatives — build internal developer platforms / self-service tooling that reduce cognitive load for application teams (standardized environments, templated deployments, golden paths).
• Own the technical pipeline for the Secure Software Development Lifecycle (SSDLC), including security gates at each stage of build and release.
• Drive vulnerability mitigation — triage findings from scanning tools and coordinate timely remediation across teams.
• Own patch management — track, prioritize, and apply patches across infrastructure and dependencies on a defined cadence.
• Bring AI into operations (AIOps) — apply anomaly detection, intelligent alerting, and automation to reduce noise and enable self-healing pipelines and infrastructure; use AI-assisted engineering tools (e.g., Cursor, Claude Code) daily as a core part of how you build and ship.
• Own observability — instrument systems for metrics, logs, and traces; build dashboards and alerting that reflect real business and system risk, not just infrastructure noise.
• Take responsibility for cost (FinOps) — monitor cloud spend, rightsize resources, and build budget guardrails across environments.
• Support MLOps/AI infrastructure needs as our AI tooling and agentic workflows scale — model/version registries, secure API access patterns (e.g., workload identity federation), and sandboxing for autonomous agents.
• Act as a technical bridge between development, security, operations, and business stakeholders — explain trade-offs clearly, coordinate incidents calmly, and contribute to architecture discussions, not just implementation.

What You Bring
Core technical foundation!!!
• 5+ years in DevOps/SRE/Platform Engineering roles, with at least 2 years operating at a senior/lead level (system design, mentoring, on-call ownership). Strong hands-on DevSecOps experience, including SAST, DAST, SCA, container image scanning, secrets management, vulnerability remediation, security policy enforcement, and Secure Software Development Lifecycle (SSDLC).
• Deep hands-on experience with at least one major cloud provider (Azure or GCP strongly preferred given our stack; AWS a plus), including IAM, networking, and workload identity patterns. Strong understanding of networking fundamentals, including TCP/IP, HTTP/HTTPS, TLS, UDP, DNS, load balancing, ingress, and cloud networking architectures.
• Strong command of containerization and orchestration: Docker, Kubernetes in production, Helm.Strong Linux administration skills, including file permissions, ownership, process management, networking tools, shell scripting, and troubleshooting production systems.
• Proven experience with Infrastructure as Code (Terraform preferred) and GitOps workflows (ArgoCD, Flux, or equivalent).
• Solid scripting/automation skills (Python, Bash, or Go) and comfort reading/writing CI/CD pipeline configs (GitHub Actions, GitLab CI, Jenkins, or Azure DevOps).
• Required: daily hands-on experience with AI-assisted coding tools (Cursor, Claude Code, GitHub Copilot, or similar) as part of your development and infrastructure workflow — this is a must-have, not a nice-to-have. Experience with modern AI platforms or services such as Amazon Bedrock, Azure AI Foundry, Vertex AI, OpenSRE, HolmesGPT, LangChain, MCP, or similar is highly desirable.
Security & compliance
• Practical DevSecOps experience: vulnerability scanning (Snyk, OWASP Dependency-Check or similar), secrets management, and policy-as-code/compliance tooling.
• Understanding of zero-trust network design and enterprise IAM governance (relevant to regulated, multi-entity organizations).
Emerging / 2026-relevant skills
• Familiarity with AIOps concepts and tools — using AI/ML signals for incident detection, capacity planning, or pipeline self-healing.
• Working knowledge of platform engineering principles and building internal developer platforms (IDPs).
• Exposure to MLOps or AI infrastructure (GPU scheduling, model registries, agent sandboxing/network isolation) is a strong plus given our growing AI governance and agentic tooling initiatives.
• Comfort operating in a FinOps mindset — cost visibility and optimization as a shared responsibility, not a separate team's job.
Soft skills (non-negotiable at senior level)
• Strong written and verbal communication — able to explain technical trade-offs to both engineers and non-technical stakeholders.
• Calm, structured incident coordination under pressure.
• A systems-thinking mindset: understanding failure modes and designing for resilience rather than reacting to outages.
• Collaborative, low-ego approach to working across dev, security, QA, and business teams.

Nice to have
• Experience in real estate, financial services, or another regulated, multi-entity enterprise environment.
• Familiarity with SAP-adjacent integration landscapes or large-scale enterprise system migrations.
• Cloud certifications (Azure DevOps Engineer Expert, Google Professional Cloud DevOps Engineer, or equivalent).

 

We are looking for a Senior Data Engineer to design and build scalable data platforms that process large volumes of data for analytics, reporting, and business-critical applications.

You will work with distributed data processing systems, cloud infrastructure, streaming platforms, and modern data pipelines. We are looking for an engineer who can independently own production solutions and solve complex performance and architecture challenges.

Mandatory Skills

Programming & Data Processing

• Strong Python development skills
• Advanced SQL (complex queries, optimization, execution plans)
• Strong commercial experience with Apache Spark / PySpark
• Experience optimizing Spark jobs (partitioning, shuffle, joins, skew handling)

Data Engineering

• Experience building production-grade ETL / ELT pipelines
• Strong experience with Apache Airflow
• Strong hands-on experience with Apache Kafka
• Understanding of batch and streaming data processing architectures

Infrastructure & Storage

• Experience with relational databases (PostgreSQL, MySQL or similar)
• Data modeling and query optimization
• Hands-on experience with at least one cloud platform (AWS, Azure or GCP)
• Experience working with Docker, Linux, Git, and basic CI/CD pipelines

Desired Skills

• Databricks, Snowflake or similar cloud data platforms
• Kubernetes
• CDC pipelines (Debezium or similar)
• Modern analytical/storage systems (ClickHouse, Trino, Iceberg, Delta Lake)
• Monitoring tools (Grafana, Prometheus)

Responsibilities

• Build scalable batch and streaming data pipelines
• Develop and optimize Spark/PySpark data processing jobs
• Build Kafka-based ingestion and event-driven pipelines
• Design and maintain Airflow orchestration workflows
• Optimize SQL queries and improve pipeline performance
• Investigate production issues and improve system reliability
• Participate in architecture and technical design discussions

Senior-Level Expectations

• Independently design scalable data pipelines
• Debug and optimize distributed production systems
• Solve performance bottlenecks in Spark and SQL workloads
• Make technical decisions with minimal supervision
• Take ownership of production solutions end-to-end

Soft Skills

• Strong problem-solving skills
• Ownership and accountability
• Ability to work independently
• Good communication and collaboration skills
• Proactive engineering mindset

The CHI Software team is not standing still. We love our job and give it one hundred percent of us! Every new project is a challenge that we face successfully. The only thing that can stop us is… Wait, it’s nothing! The number of projects is growing, and with them, our team too. And now we need a Middle/Senior Data Engineer.
 

Requirements:

• 4+ years of experience as a Data Engineer;
• Completed Data Engineering Professional certification;
• Minimum 2 projects delivered with hands-on experience in development on Databricks;
• Experience in Apache Spark Programming with Databricks;
• Experience with Azure cloud;
• Upper-Intermediate English level.

We are looking for a Senior Data Engineer Consultant to join an AI consulting engagement focused on LLM architecture, RAG, data strategy, and AI platform advisory.
 

This is an advisory role rather than a full-time implementation position. The consultant will work with AI architects, ML engineers, MLOps specialists, and the client’s engineering team to assess the current data landscape, review proposed solutions, and provide practical technical recommendations.
 

Responsibilities
 

Data Strategy
 

• Review the existing enterprise data landscape and available data sources.
   
• Assess data readiness for foundation model training, fine-tuning, evaluation, and RAG.
   
• Recommend data acquisition, prioritization, and preparation approaches.
   
• Advise on multilingual data preparation strategies.
   

Data Pipelines
 

• Review the current ETL/ELT architecture and data processing workflows.
   
• Recommend pipeline designs for model training, inference, and evaluation.
   
• Evaluate orchestration, dataset versioning, and data traceability approaches.
   
• Assess pipeline scalability, reliability, maintainability, and performance.
   

RAG Data Engineering
 

• Review document ingestion architecture for enterprise knowledge bases.
   
• Recommend parsing, chunking, metadata enrichment, embedding, and indexing approaches.
   
• Evaluate vector database and search integration strategies.
   
• Advise on incremental updates, synchronization, access-aware retrieval, and retrieval optimization.
   

Data Governance and Quality
 

• Review data lifecycle management and data governance practices.
   
• Assess PII handling, data masking, retention policies, access control, lineage, and auditability.
   
• Recommend data validation and quality-control frameworks.
   
• Review training and evaluation datasets.
   
• Advise on monitoring data freshness, consistency, completeness, and data drift.
   

Documentation and Consulting
 

• Prepare technical recommendations, architecture assessments, and implementation guidelines.
   
• Review technical documentation, diagrams, and proposed designs.
   
• Participate in architecture reviews, design discussions, and technical workshops.
   
• Conduct knowledge-transfer sessions with the client’s engineering team.
   

Technology Stack
 

Python, SQL, Apache Airflow, Apache Kafka, Hadoop, Apache Superset, n8n, ETL/ELT, RAG Pipelines, Vector Databases, Data Governance, Cloud (AWS, Azure or GCP).
 

Preferred Domain Experience
 

Experience in banking, fintech, financial services, or another highly regulated domain is highly desirable.
 

The ideal candidate should understand domain-specific requirements related to:
 

• sensitive and confidential data;
   
• PII protection and data masking;
   
• role-based access control;
   
• data retention and auditability;
   
• regulatory and compliance requirements;
   
• secure use of enterprise data in AI and LLM solutions.
   

Engagement Details
 

• Engagement model: Part-time consulting.
   
• Workload: Flexible and non-uniform, depending on the current project phase.
   
• Expected activities: Architecture reviews, workshops, document reviews, technical assessments, and advisory sessions.
   
• Duration: Approximately 6 months.
   
• Project type: AI/LLM consulting engagement.
   
• Client interaction: Direct collaboration with the client’s engineering and architecture teams.
   
• Availability: The consultant should be able to join scheduled sessions and provide asynchronous reviews between meetings.
   

EverHelp is a team of professionals, that’s developing outsourcing support services for plenty of products all over the world at a dynamic tempo. The project launched in 2021, and last year we experienced a yearly growth rate of +82%. Since February 2022, we've managed to preserve all our workplaces and, most importantly, we continue to strengthen our team while maintaining an employee satisfaction rate of over 90%.

In 2026, EverHelp received 9 international workplace and employer awards. The company ranked #1 in the Top 30 Inspiring Workplaces in Europe, was named the #1 employer among European companies with 500–4,999 employees, and earned Best-in-Class recognition across 6 categories: Culture & Purpose, Leadership, Inclusion & Belonging, Wellbeing, Employee Voice, and Employee Experience. EverHelp was also named one of the 28 Best Places to Work by Business Intelligence Group — a global award recognizing companies with strong workplace cultures and outstanding employee experience.

Our statement:

• We’re against war and unjustified aggression,
• We evacuated all the teammates and their families from the frontline, helped them find a new place to live and provided financial support,
• We provided the team with charging stations,
• We continue to work together for the future of Ukraine.

Your future responsibilities include:

• Building, optimizing, and maintaining ETL pipelines that extract data from ERP
  systems, databases, and APIs, transform it into a usable format, and load it into
  BigQuery
• Automating data ingestion, transformation, and loading processes to reduce manual
  intervention and ensure efficiency
• Monitoring and troubleshooting data pipelines proactively — catching performance issues
  issues, errors, and data quality problems before they escalate
• Managing and optimizing data storage: partitioning, indexing, and performance tuning
  in BigQuery
• Designing data models that represent the structure and relationships within a business
  data
• Implementing data quality checks and validation rules at every stage of the pipeline
• Identifying and resolving data inconsistencies, errors, and missing values
• Conducting data audits and comparing results with business user needs — proposing
  solutions to close existing gaps
• Collaborating with analysts, data scientists, and business stakeholders to understand
  their data needs and translate them into scalable solutions

Why this role might be interesting for you:

If you love structure and enjoy optimizing processes, this is your playground. You'll make real-world architectural decisions, not just execute tickets. Every pipeline you build or improve directly powers business decisions at the highest level, so the impact of your work is visible and immediate. And if you want to be the person who shapes a data-driven culture from the inside — that's exactly the role we're offering.

Needed experience & skills:

• 2-4+ years of hands-on experience as a Data Engineer
• Advanced knowledge of SQL and Python
• Hands-on experience with GCP, in particular BigQuery
• Strong ETL and data warehousing skills: Apache Airflow, dbt
• Clear understanding of both the technical and business sides of data products
• Clear and concise communication; comfortable working cross-functionally with nontechnical stakeholders
• Proactive problem-solving mindset — analytical thinking and attention to detail
• English level — B1-В2

Nice to have skills:

• Experience at a large systematic company (retail, banking, telecom, FMCG, logistics)
• Comfort working autonomously as the only Data Engineer on a team
• Adaptability in a high-paced, fast-changing environment

Hiring Process:

CV Screening → Questionnaire → Intro Call → Tech & Soft Interview → Bar Raising

Work with EverHelp is about:

• Opportunity to join the Ukrainian company and help the Ukrainian economy,
• 20+ vacation days and unlimited sick leaves, corporate doctor & psychologist,
• Medical insurance and +10 Healthcare leaves for mental & physical recovery,
• Systematical learning & development inside a team and on an individual level: compensation for professional webinars, courses, conferences, etc.,
• Ability to work from Lviv & Kyiv offices with free breakfasts and lunches or fully remotely,
• Team of professionals who’s ready for knowledge sharing, internal professional communities, team buildings,
• Corporate discounts for gym membership and for other different services like medical services, language schools, etc.

Submit your resume and join our team!

Tieto Tech Consulting, is looking for a highly skilled and motivated Junior Telco Engineer, a professional who will join a project within a Telecom Team for our respectful client BICS.

Join the team of our customer BICS – a global telecommunication enabler with a physical network stretching through the globe! Grow your expertise through interacting with A-level mobile carriers from all over the world. Get your hands on the vast network with on land, underwater, and space points of presence.Join our decade-long partnership with BICS to ensure confidence in your future and long-term growth! Tieto-BICS team has been growing since 2010… and we are looking forward to grow with you!

• Develop your telecom expertise in diverse portfolio of BICS products, including Voice, Cloud, Signaling, LTE, IoT, etc.
• Enjoy unmatched growth opportunities with BICS vast network and customers around the world.

Tieto - BICS team has been growing since 2010… and we are looking forward to growing with you!

Responsibilities

• Ensure accurate documentation of the Fixed Services product portfolio (e.g. Voice, VoIP, Signaling, Capacity) in inventory tools, based on project low-level design.
• Maintain both service and network data by creating and updating low-level configurations in dedicated inventory systems, in line with internal routing guidelines.
• Collaborate with Regional Technical Managers (RTMs) and support both high-level and low-level design activities for projects (e.g. TDM/VoIP implementations).
• Ensure data quality and consistency, and support continuous improvement and cleanup of documentation.

Qualifications

• Experience in a mobile or fixed telecom operator is an asset.
• Understanding of telecom networks and topology, including core network elements and their functions.
• Knowledge of service fulfillment and telecom operational processes is an advantage.
• Familiarity with protocols and services such as voice switching and routing (TDM, VoIP), transmission (SDH/IP), and basic SS7 signaling.
• Interest in working with configuration data across different systems and tools.
• Strong analytical skills, attention to quality, and results-oriented mindset.
• Good command of spoken and written English.

About the role:

As a Senior Data Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.

Design, build, and optimize enterprise data pipelines and lakehouse solutions aligned to client’s data architecture, governance standards, and cloud-first strategy. Partner with cross-functional teams to deliver secure, scalable, and high-quality data solutions that enable analytics, reporting, and AI-driven capabilities.

Responsibilities:

• Design, develop, and maintain scalable ETL/ELT pipelines across enterprise systems including ERP, CRM, and operational platforms 
• Build and support client’s cloud-based data lakehouse architecture (aligned to medallion layers and governance standards) 
• Develop and maintain data models, semantic layers, and curated datasets for analytics and business intelligence 
• Translate business requirements into technical data solutions in collaboration with product, engineering, and business stakeholders 
• Ensure data quality, integrity, and security in alignment with client;s data governance and compliance standards 
• Optimize SQL queries, data pipelines, and storage performance across AWS and Azure platforms 
• Support data integration initiatives including Data Lake, D365 ERP, and CRM/AMS platforms 
• Monitor pipeline health, troubleshoot issues, and ensure high availability and reliability of data systems 
• Contribute to continuous improvement of data architecture, standards, and best practices 

• Support AI and analytics initiatives by enabling high-quality, accessible data for downstream consumption 

   

Technical Requirements (Must Have):

• Bachelor’s degree in Computer Science, Information Systems, or related field 
• 4+ years of experience in data engineering or data platform development 
• Strong experience designing and building ETL/ELT pipelines in cloud environments 
• Hands-on experience with AWS (S3, Redshift, Glue, DMS) and Azure (Data Factory, SSIS) 
• Strong proficiency in SQL and relational database design 
• Solid understanding of data warehousing, data lakes, and lakehouse architecture 

• Experience with structured, semi-structured, and unstructured data processing 

   

Non-Technical Requirements (Nice to Have):

• Ability to establish positive working relationships with multiple disciplines of Information technology department & staff levels
• Demonstrated ability to collaborate and receive feedback regarding ongoing projects
• Demonstrated ability to respond to business issues with the appropriate sense of urgency
• Sense of when to escalate a problem or ask for assistance
• Organized, self-starter with outstanding written and verbal communication skills
• Problem solving skills and strong attention to details
• Ability to identify and document business/system requirements
• Ability to work in a high energy, team focused environment
• Ability to work and deliver against aggressive timelines to meet the project schedules

• Ability to work productively from home (including access to a reliable internet connection) is required

   

Location and Hiring Model

• Locations ONLY: Bulgaria
• Hiring Model: Direct Hire ONLY (B2B)

About the role:
We're looking for an experienced engineer to design and build enterprise knowledge graphs and semantic data layers. You'll work at the intersection of data architecture, AI, and business domains — formalizing business knowledge into machine-readable models.

Responsibilities:

• Design, develop, and implement enterprise-grade knowledge graphs
• Build semantic layers that establish shared business understanding across enterprise systems
• Model business entities, relationships, taxonomies, ontologies, and controlled vocabularies
• Create semantic data products for AI applications, analytics platforms, and business users
• Design enterprise metadata models and integrate them with data catalogues
• Connect structured and unstructured enterprise data within a unified semantic model
• Maintain governance, lineage, metadata quality, and semantic consistency

Requirements:

• Hands-on experience designing and implementing knowledge graphs
• Semantic technologies: RDF, OWL, SKOS, SPARQL
• Graph databases: Neo4j, Amazon Neptune, Stardog, GraphDB, or equivalent
• Enterprise metadata management & data catalogue platforms
• Experience developing ontologies, taxonomies, and controlled vocabularies
• Strong enterprise data architecture and data modelling knowledge
• Advanced SQL and multi-system data integration
• Cloud platforms: Azure, AWS, or GCP

Nice to have:

• Semantic or enterprise search solutions
• Master Data Management (MDM)
• Data Mesh / Data Product concepts
• Enterprise data governance frameworks
• AI agents, GenAI, or RAG architectures

About AppsFlyer:

AppsFlyer provides a SaaS marketing analytics platform that measures and optimizes marketing activities across mobile, web, CTV, PC, and console applications. AppsFlyer is known for its massive backend production. At any given moment, thousands of servers are consuming 150+ billion mobile app events, crunching our users’ data, serving requests, and communicating on a massive scale.

To maintain the universe we call AppsFlyer, we practice modern production operations with a complete self-serve CI/CD platform, a highly integrated observability stack for our microservices, backends, and infrastructure, and a culture of ownership and eagerness for quality.

The Analytics group is responsible for showing our clients the stories their data is telling. Through complex aggregation, mission-tailored analytical databases, and carefully crafted APIs, we are able to provide slice-and-dice analysis in our beautiful dashboards and through the use of external integrations.

About the Role:

As a Data Engineer(Big Data) within the Analytics group at AppsFlyer, you will own the end-to-end journey of event data - from ingestion and processing to storage, modeling, and API delivery. 

You will design and implement data pipelines using Apache Airflow and Spark, choose and optimize analytical databases, and integrate new components in our hybrid microservices environment. You will collaborate with product managers and cross-functional teams, troubleshoot production incidents, and influence our migration toward Google Cloud and BigQuery. This role offers autonomy over feature design, real impact on a platform handling billions of events daily, and the chance to contribute to open-source projects or speak at industry forums.

Key Responsibilities:

• Develop and maintain data processing pipelines using Apache Airflow, Scala Spark or PySpark.
• Select, model and optimize analytical databases for high-throughput, low-latency queries.
• Design and implement backend services and APIs for data ingestion, aggregation and client consumption.
• Monitor system performance and stability; diagnose and resolve production issues.
• Collaborate with product managers and engineers to plan and deliver complex features.
• Drive migration efforts to Google Cloud Platform, BigQuery and modern data tooling.
• Participate in on-call rotations, alerting via PagerDuty and incident response procedures.
• Document system architecture and data flows; maintain code quality and tests.

Required Competence and Skills:

• 3+ years of hands-on experience building and operating production Big Data systems
• 2+ years of proven experience designing, developing, and maintaining large-scale distributed data processing pipelines
• Strong practical experience with distributed data processing technologies such as Apache Spark or equivalent Big Data frameworks.
• Experience with workflow orchestration tools such as Apache Airflow or similar.
• Strong SQL skills and experience designing data models for analytical workloads.
• Familiarity with JVM languages (Scala, Java, Clojure) or Python for backend development.
• Experience working with cloud platforms and modern data warehouses like Google Cloud Platform and BigQuery or similar.
• Experience owning production systems, including monitoring, troubleshooting, and responding to production incidents.
• Strong communication skills and ability to collaborate effectively within distributed engineering teams.
• B.Sc. in Computer Science or equivalent practical experience.

Nice to Have:

• Production experience with Google BigQuery, including ETL pipelines and analytical workloads.
• Experience developing backend services in Go or Clojure.
• Experience contributing to open-source projects or presenting at technical conferences or meetups.
• An AI mindset, including practical use of tools such as GitHub Copilot, Cursor, ChatGPT, or similar AI-assisted development workflows.

Benefits Package includes:

• Health Insurance
• Paid Unlimited Vacation Days + all national holidays + additional recharge days
• Meals Reimbursement
• Sport Reimbursement
• Breakfast in the office
• Mental health program
• Team building, happy hours, and other team activities
• Paid sick days
• Snacks, fruits & ice-cold beer
• All new team members are provided with a brand-new Mac laptop with 2 monitors and a Starter package

We're looking for an experienced Senior Backend Engineer to help build and operate an AI-powered software development platform for a public-sector client. The platform combines LLM-based code generation, AI model integrations, cloud-native backend services, and automated development workflows in a scalable production environment.
 

If you're passionate about distributed systems, modern JVM technologies, cloud infrastructure, and building secure identity-driven platforms, we'd love to hear from you!
 

Core Competence:
 

• 5+ years of professional backend development experience with strong software engineering fundamentals and experience building scalable distributed systems.
• Strong proficiency in Kotlin and/or Java with hands-on experience developing backend applications on the JVM ecosystem.
• Deep experience with Spring Boot / Spring Framework for designing and implementing production-grade microservices.
• Strong understanding of microservices architecture, including service boundaries, API design, inter-service communication, resilience patterns, and distributed system challenges.
• Solid experience with PostgreSQL, including database modeling, schema design, query optimization, and migration management.
• Experience with JUnit for unit and integration testing, with a strong focus on code quality and reliability.
• Hands-on experience with Docker for containerization, image creation, and optimization.
• Strong knowledge of Kubernetes (K8s) for deploying, scaling, and operating cloud-native applications.
• Experience with Helm for Kubernetes package management and deployment automation.
• Proficiency with Gradle for build automation and dependency management.
• Experience with Git/GitLab workflows, including branching strategies, CI/CD pipelines, and code review processes.
• Practical knowledge of OpenID Connect and experience implementing authentication and authorization flows.
• Experience with Keycloak or Ory products, including IAM configuration, realms, clients, and identity flows.
   

Preferred Qualifications:
 

• Experience working with AI-powered platforms, LLM integrations, or AI-assisted development tools.
• Background in digital identity and verifiable credentials ecosystems.
• Knowledge of modern identity standards, including:
  • OpenID4VP — presentation of verifiable credentials.
  • SD-JWT — selective disclosure of individual attributes.
  • OpenID4VCI / OpenID4VC — issuance and management of digital credentials.
  • mdoc (ISO/IEC 18013-5) — mobile document formats such as mobile driver's licenses.
• Experience building secure systems for government, enterprise, or regulated environments.
• Understanding of cloud-native architecture principles and production operations.

• Experience contributing to early-stage platforms and evolving solutions from initial concepts into reliable production systems.

   

What We Offer You:
 

• Cutting-Edge AI Projects: Work on a platform combining LLM technologies, backend engineering, and cloud-native infrastructure.
• Technical Impact: Influence architecture decisions and contribute to building scalable solutions used in real-world environments.
• Modern Engineering Culture: Work with advanced technologies, automation practices, and strong engineering standards.
• Professional Growth: Opportunity to deepen expertise in AI platforms, distributed systems, and digital identity technologies.
• Global Collaboration: Join an international team working on challenging and impactful projects.
• Flexible Work Environment: Remote-friendly setup with a focus on autonomy and work-life balance.

About the Product

We are building a US-based, data-driven product with a strong focus on scalability, performance, and cost efficiency.

Our mission is to design a modern data platform that transforms raw behavioral and monetization data into reliable, actionable business insights — in near real-time.

For us, data engineering is not just about moving data.
It’s about:

• Designing resilient architecture
• Optimizing for performance and cost
• Building reliable automation
• Ensuring architectural integrity at scale
•  

Role Overview

We are looking for a Senior Data Engineer who will take ownership of the data platform architecture and drive technical excellence across ingestion, modeling, and performance optimization.

This role requires deep expertise in SQL, Python, AWS infrastructure, and modern data stack principles. You will not only build pipelines — you will define standards, lead architectural decisions, and proactively improve system efficiency.

You will play a critical role in ensuring that data flows seamlessly from event streams to business-ready datasets while maintaining high performance, reliability, and cost control.

What Makes This Role Senior-Level

As a Senior Data Engineer, you will:

• Own architectural decisions for the data platform
• Identify scalability bottlenecks before they become incidents
• Optimize data infrastructure for performance and cost
• Lead technical code reviews and set engineering standards
• Mentor mid-level engineers
• Act as a technical partner to Product and Analytics stakeholders
• Balance real-time and batch processing strategies strategically

Technical Requirements

Must-Have

Expert-Level SQL

• Complex analytical queries and window functions
• Query optimization and execution plan analysis
• Identifying and eliminating performance bottlenecks
• Reducing query complexity and compute costs
• Designing partitioning and clustering strategies

Python

• Advanced data manipulation
• Building scalable ETL/ELT frameworks
• Writing production-grade data services
• Automation and monitoring scripts

AWS Core Infrastructure

• AWS Kinesis Firehose (near-real-time data streaming)
• Amazon S3 (data lake architecture and storage optimization)
• Designing reliable ingestion layers

Version Control

• Git (GitHub / GitLab)
• Branching strategies
• Leading technical code reviews
• Enforcing best practices in code quality

Nice-to-Have

Modern Data Stack

• dbt (modular SQL modeling, documentation, testing)
• Experience structuring layered data models (staging → intermediate → marts)

Data Warehousing

• Google BigQuery
• Slot management
• Cost-efficient querying
• Storage and compute optimization

Advanced Optimization Techniques

• Partitioning
• Clustering
• Bucketing
• Storage layout optimization

Integrations & Infrastructure

• Salesforce data integration
• Docker / ECS
• CI/CD for data workflows

AI / ML Exposure

• Supporting feature pipelines
• Understanding data requirements for ML systems

Key Responsibilities

Data Platform Architecture

• Design and maintain a scalable real-time and batch data platform
• Architect ingestion pipelines using AWS Kinesis and Python
• Ensure high availability and reliability of data flows

Real-Time Processing

• Enable near-real-time (seconds–minutes latency) data processing
• Build systems for operational alerting and anomaly detection
• Ensure early detection of monetization and traffic issues

Data Modeling

• Transform raw event data into business-ready datasets using dbt
• Design scalable, maintainable schemas aligned with product evolution

Performance & Cost Engineering

• Optimize SQL queries and storage structures
• Design cost-efficient partitioning strategies
• Monitor and reduce warehouse and infrastructure costs
• Balance real-time and batch processing appropriately

Engineering Excellence

• Lead and participate in code reviews
• Enforce high standards of performance, security, and maintainability
• Improve observability and monitoring across pipelines

Cross-Functional Collaboration

• Work closely with Data Analysts and Product Managers
• Translate business requirements into scalable technical solutions
• Clearly communicate trade-offs between speed, cost, and complexity

Type of Data We Process

• User behavior events (page views, clicks, searches, conversions)
• Ad & monetization events (impressions, clicks, CTR, attribution)
• System and integration logs (latency, errors, rate limits)

Why Real-Time Is Critical

• Detect broken ads or impression drops before revenue is lost
• Identify traffic anomalies or abuse early
• Enable same-day operational intervention
• Prevent negative user and advertiser experience

Near-real-time (seconds to minutes latency) is required for operational awareness.
Batch processing remains important for historical analysis and reporting — but not for incident detection.

Working Schedule

• Monday – Friday
• 16:00 – 00:00 Kyiv time
• Full alignment with US-based stakeholders

What We Value

• Strong ownership mindset
• Strategic thinking about architecture
• Focus on scalability, reliability, and cost efficiency
• Proactive problem-solving
• Clear communication with both technical and non-technical teams
• Ability to think beyond “just making it work”

Project overview

This project focuses on building and enhancing an enterprise data warehouse platform that supports data driven decision making across complex business domains. The work involves designing scalable data models, ingestion pipelines, and transformation logic to ensure high quality and consistent data.

Team

You will be part of a collaborative data team working alongside data designers, data engineers, and platform specialists. The team operates in an agile environment, shares knowledge actively, and follows established data design standards and review practices.

Position overview

We are looking for a Senior Data Designer who can operate as the bridge between business stakeholders and engineering — someone equally comfortable running a requirements workshop and designing a Kimball-style dimensional model. This is not a pure modelling role: you'll investigate source systems, challenge and shape business requirements before any design work starts, and turn that understanding into governed, well-documented data platform designs that engineering teams can implement. You will contribute to data ingestion design, transformation logic, and data harmonization while working closely with stakeholders and platform teams.

Technology stack

Core: SQL, dimensional/Kimball data modelling, enterprise data warehouse tools, cloud data platforms
Additional technologies used within the project: Python, PySpark, data profiling tooling

Responsibilities

• Design and document Enterprise Data Warehouse objects: data models, source-to-target mappings, model diagrams, and supporting design documentation that engineers deliver from
• Work with stakeholders and source-system SMEs to onboard new data sources and adapt designs to what each source provides
• Clarify the intent behind requirements before designing: why the stakeholder needs it and whether existing structures already cover it
• Profile and analyze source data to validate design assumptions
• Define data quality rules as part of designs for downstream teams to implement
• Investigate reported data issues, identify root causes, and propose design-level fixes
• Take designs through peer and design review; give and take constructive challenge
• Keep stakeholders informed: raise issues early, ask instead of assuming, follow up until unblocked

Requirements

• Strong practical foundations of dimensional modeling — able to design warehouse models from raw source data and explain the reasoning behind design choices
• Hands-on experience producing source-to-target mapping documentation
• Working understanding of data platform layering and data load approaches
• Confident SQL for data profiling and validation
• Strong written and spoken English; comfortable gathering requirements and managing stakeholder expectations
• Attention to detail; comfortable working in a governed environment with reviews and design standards, and data privacy/PII classification requirements

Nice to have

• SAP as a source system
• Retail domain experience
• Agile delivery experience

Що варто знати про SKELAR? Це венчур-білдер — іншими словами, компанія, яка будує компанії. З нами фаундери створюють consumer-бізнеси, які стають лідерами на глобальних ринках EdTech, Mental health, Petcare, FashionTech та інших.

Скеларівці будують tech-продукти, які здобувають відзнаки від Product Hunt, The Webby Awards та отримують тисячі позитивних відгуків на TrustPilot. Загалом tech-продуктами наших бізнесів користуються 10+ млн людей у світі. 
Усе завдяки сильним командам, бо наші спеціалісти — це ключова цінність SKELAR.

Для професійної реалізації ми створили середовище з експертизою та необмеженими можливостями — і шукаємо тих, хто прагне викликів.

Що ти будеш робити

• Data Pipelines: Будувати та масштабувати надійні ETL/ELT-процеси для збору та обробки даних про активність користувачів
• Архітектура та інфраструктура: Проєктувати хмарні сховища даних та аналітичні архітектури на базі сучасних data-інструментів (BigQuery, Airflow, Dataform)
• Data Quality: Забезпечувати високу доступність, точність та безпеку обробки даних; дотримання стандартів якості на рівні платформи — включно з data lineage та observability
• Крос-функціональна співпраця: Працювати разом із командами аналітики, бекенду та інфраструктури — перетворювати сирі дані на продуктові метрики та actionable insights

Що ми очікуємо

Hard skills:

• Глибоке володіння Python та SQL: від побудови production-ready рішень до оптимізації складних запитів під реальне навантаження
• Практичний досвід із ETL/ELT-оркестрацією (Airflow, Dataform або аналоги)
• Досвід роботи з хмарними платформами (GCP, AWS або Azure)
• Розуміння data warehousing: dimensional modeling, партиціонування, кластеризація, cost control
• 5+ років як Data Engineer у продакшн-середовищах з реальним навантаженням

Підхід до роботи:

• Вмієш формулювати та відстоювати технічні рішення
• Готовий брати відповідальність за результат напрямку, а не тільки за свій код
• Проактивно підтягуєш колег — ділишся знаннями, робиш ревʼю, яке дає цінність
• Дієш як CEO у своїй ролі: бачиш картину цілком, не чекаєш задачі — формуєш порядок денний

Ми не прив'язані до стеку. Якщо ти вирішував схожі задачі іншими інструментами — розкажи як. Нам важливий твій підхід до проблем і здатність швидко розбиратись у технологіях та підходах.

Буде плюсом

• Досвід на стороні аналітики (Data Analyst) — розумієш, що потрібно споживачам даних
• Досвід у ролі Python Engineer поза data-контекстом

У SKELAR ми побудували середовище для професійної самореалізації:

• SKELAR Platform. Це операційне ядро венчур-білдера, що допомагає фаундерам швидше проходити шлях від нуля до одиниці. Так команди бізнесів фокусуються на продукті — а 10+ команд в складі Platform закривають функції Legal, Finance, HR, Admin, Talent Acquisition, Employer Branding, IT та інші.
• Нетворк та обмін досвідом як частина культури. Серед наших колег — кофаундери, які запустили не один бізнес, а також найсильніші спеціалісти на ринку. Тож ми маємо доступ до необхідної експертизи на відстані витягнутої руки.
• Ексклюзивна доменна освіта. Ми створили власні навчальні програми для тіммейтів на вибір — від 2-місячної школи для менеджерів-початківців до інтенсивів про AI та професійних клубів з розробки, продакт-менеджменту тощо.
• Вплив на системну допомогу Україні. Маємо благодійний фонд SKELAR foundation, який створили самі скеларівці — і завдяки кожному співробітнику втілюємо важливі соціальні проєкти. Наприклад, заснували перший у Європі акселератор для ветеранів і формуємо майданчик для підтримки ветеранів на рівні держави.
• Екосистема для продуктивної роботи. Для скеларівців створені простори, які надихають на результат — з власним спортзалом, скеледромом, зеленими зонами Bali та бібліотеками. У спейсах, де сервіс, безпека та комфорт синхронізовані з темпом зростання команд, отримуємо вау-досвід та працюємо ефективніше.
• Турбота про щоденний комфорт. Сніданки, обіди, доступ до спортивних тренувань в Києві або часткова компенсація занять у Варшаві, медичне страхування, оплачувані відрядження, якщо специфіка ролі передбачає такі поїздки, корпоративні знижки від партнерів.

Більше розповімо на нашій зустрічі-знайомстві.

Запрошуємо будувати the next big everything у SKELAR разом!

Шукаємо Principal Data Engineer

📍 Віддалено з України
💼 Повна зайнятість
🚀 Рівень: Principal / Staff

Про проєкт

Міжнародний технологічний проєкт поєднує OSINT та штучний інтелект, допомагаючи державним установам отримувати критично важливу інформацію про технологічні й економічні екосистеми недружніх країн.

Платформа перетворює відкриті дані на структуровану аналітику, яка підтримує ухвалення стратегічних рішень. Зараз команда суттєво розширює свій інженерний напрям.

Про роль

Ви приєднаєтеся до команди обробки даних, яка перетворює необроблену інформацію на чисті, структуровані та надійні набори даних.

Ви відповідатимете за весь життєвий цикл пайплайнів: від завантаження сирих даних у bronze-рівень, entity resolution та збагачення — до gold-датасетів, які використовуються у продукті, графі знань та комерційних експортних рішеннях.

Це роль із високим рівнем відповідальності та самостійності: ви проєктуватимете пайплайни й моделі даних, визначатимете технічні стандарти та впливатимете на розвиток команди.

Масштаб системи:

🔹 понад 1 млрд точок даних
🔹 граф знань із понад 50 млн сутностей
🔹 компанії, люди, закупівлі та зв’язки між ними

Ваші основні завдання:

• Побудова й підтримка end-to-end пайплайнів у medallion-архітектурі: bronze → silver → gold
• Розвиток моделей компаній, людей і зв’язків між ними
• Перехід від повного оновлення даних до інкрементальної обробки
• Оркестрація та трансформація даних за допомогою Dagster, dbt, Trino і Databricks
• Розвиток gold-рівня для продукту, метрик і комерційних експортів
• Співпраця з командами, які завантажують дані в Neo4j та Elasticsearch
• Розвиток аналітичного шару й датасетів для Metabase
• Забезпечення якості даних, надійності, спостережуваності та оптимальної вартості обробки
• Технічне менторство та участь у формуванні інженерних практик команди

Що важливо:

• Досвід на рівні Principal або Staff у побудові та експлуатації production data pipelines великого масштабу. Також розглядаємо сильних Senior-інженерів, готових до наступного кроку
• Впевнене володіння Python і SQL
• Досвід із сучасним data stack: оркестрація, SQL-first трансформації та розподілена обробка даних
• Глибоке розуміння data modelling, medallion/lakehouse-підходів, dimensional та incremental models, schema evolution, data quality і тестування
• Досвід роботи з хмарними платформами та контейнеризованими середовищами. Поточний стек: Azure, Databricks та ADLS
• AI-first підхід: використання Claude Code, Codex або інших агентних інструментів із відповідальністю за перевірку результатів
• Самостійність, сильна письмова англійська та вміння посилювати команду

Буде перевагою:

• Досвід з entity resolution, record linkage або knowledge graph pipelines
• Проєктування онтологій чи семантичних моделей даних
• Знання Trino, Iceberg та lakehouse table formats
• Досвід із Neo4j і Cypher
• Analytics engineering, BI-інструменти, metrics layers і star schemas
• Data contracts та data-quality frameworks
• Робота з даними мовами недружніх країн або нормалізацією нелатинських систем письма
• Досвід у регульованих галузях, оборонному секторі, розвідці чи інших середовищах із високими вимогами до довіри й безпеки

AI є невіддільною частиною інженерного процесу. Команда активно використовує Claude Code та Codex, надає доступ до цих інструментів і очікує їх відповідального застосування в щоденній роботі.

Процес найму: 

- 3-4 співбесіди: скринінг (30-45 хв), технічна (1-2 години), презентація тестового завдання (30 хв), знайомство з CEO (30 хв)

- є тестове завдання

- співбесіди проходять англійською мовою з включеною камерою

Remote · Full-time · Перетин із робочими годинами ЄС · Principal level

Про проєкт

Проєкт поєднує OSINT і сучасну аналітику даних. Платформа допомагає державним організаціям отримувати критично важливу інформацію про технологічне та економічне середовище росії й Китаю, зокрема їхні оборонно-промислові комплекси.

Про роль

Ви станете технічним лідером Data Acquisition Team — команди, яка збирає відкриті дані з різних джерел у всьому світі.

Сьогодні ми підтримуємо десятки парсерів для складних джерел, які постійно змінюються, обмежують частоту запитів та блокують автоматизований збір. Наша мета — масштабувати систему до сотень джерел, не збільшуючи команду пропорційно, налаштовуючи процеси з використанням ШІ.

Це Principal-роль із високим рівнем відповідальності. Ви прийматимете найскладніші технічні рішення, створюватимете інструменти для всієї команди та визначатимете подальший розвиток data acquisition architecture. Водночас наявна система має залишатися стабільною, а дані — надходити безперервно.

Ваші завдання

• Створювати AI-assisted та agentic-інструменти, які перетворюють нове джерело на готові до роботи парсери.
• Масштабувати систему від десятків до сотень донорів даних.
• Розробляти self-healing механізми, які виявляють зміни джерел і збої, адаптуються та відновлюють роботу без ручного втручання в кожному випадку.
• Відповідати за всю acquisition architecture: спільні scraper/parser frameworks, proxy та stealth strategy, scheduling і передачу даних до ADLS.
• Забезпечувати стійкість до anti-bot механізмів, підтримувати міграції джерел і надійність системи в production.
• Підтримувати стабільність наявного парку парсерів і донорів під час поступової перебудови.

• Менторити інженерів, визначати технічні стандарти та розвивати AI-first підхід у команді.

   

Наш стек

Python · JavaScript · Playwright · Stealth browser tooling · VPN-based egress · Azure Data Lake Storage · Docker · Kubernetes · Azure AKS · GitLab CI · Grafana · Claude Code · Codex

Що для нас важливо

• Досвід на рівні Principal або Staff у production data collection чи web scraping у великому масштабі.
• Досвід визначення технічного напряму, а не лише реалізації готових рішень.
• Також розглянемо сильного Senior Engineer, готового перейти на наступний рівень.
• Досвід створення платформ і фреймворків, якими користуються інші інженери.
• Глибоке знання Python.
• Знання JavaScript, достатнє для reverse engineering вебсайтів: розуміння завантаження даних сторінкою, поведінки браузера та network layer.
• Практичний досвід із JavaScript-heavy сайтами, headless browsers, proxies, rate limiting та anti-bot системами.
• Практичне застосування LLM або agentic systems для генерації коду, автоматичного виправлення помилок чи автономних workflows.
• Досвід із Docker, Kubernetes і cloud environments; Azure буде перевагою.
• Розуміння cloud object storage і streaming systems як кінцевих точок для даних.
• Регулярне використання Claude Code, Codex або інших agentic coding tools зі здатністю перевіряти їхній результат і відповідати за те, що потрапляє у production.
• Системний підхід до тестування, якості та подальшої підтримки коду.
• Високий рівень самостійності, технічне лідерство та здатність посилювати команду.
• Впевнена, письмова і розмовна англійська — це основна мова комунікації.

Буде перевагою

• Production-досвід з agentic або LLM-driven pipelines для генерації коду та збору даних.
• Глибоке розуміння anti-bot механізмів, stealth crawling або роботи з CAPTCHA.
• Досвід підтримки великих multi-source scraper fleets, source lifecycle tooling або crawl scheduling.
• Робота з китайсько- чи російськомовними джерелами або вилученням даних із нелатинських писемностей.
• Досвід із data quality або entity extraction.
• Досвід у regulated, defense, intelligence або інших high-trust сферах.

AI як частина нашого engineering-процесу

Ми активно використовуємо AI-інструменти, щоб працювати швидше та підвищувати якість. Claude Code і Codex є частинами щоденного інженерного процесу: ми надаємо ці інструменти команді й очікуємо, що інженери вмітимуть ефективно їх застосовувати, перевіряти результати та відповідати за фінальне рішення.

Чому варто приєднатися

• Робота над технологічно складними проєктами у сфері геополітичної аналітики.
• Можливість побудувати масштабовану AI-driven систему збору даних.
• Високий рівень впливу на архітектуру та технічний напрям продукту.
• Сучасне AI-first engineering-середовище.
• Невелика міжнародна команда досвідчених фахівців.
• Повністю віддалена робота з регулярною взаємодією в межах європейського часового поясу.

Процес найму

4 раунди інтерв'ю англійською мовою.