Jobs Data Engineer

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.
   

FutureMedia is a dynamic, remote-first digital services company specializing in SaaS products, AI-powered solutions, and web and mobile application development. Our team brings together diverse digital experts who focus on innovation, problem-solving, and delivering impactful, technology-driven products for clients across global markets.

We design, develop, build, and analyze. We deliver impactful digital products and services — SaaS products, AI-powered solutions, web and mobile applications. As pioneers in the digital services space, we’re a team that knows how to innovate while having fun. Remote-first, but connection and collaboration are at our core.

About the Role

Our data is a strategic asset, and we are now building the infrastructure needed to unlock it. As an early member of our Data team, reporting to the Head of Data, you will design and own the pipelines, warehouse, and tooling that turn product signals into business intelligence.
You are joining at the ground floor: the data layer is being built now, so your decisions will shape it. We treat data as a product and hold a high bar for quality and reliability. The stack includes Python, SQL, dbt, Airflow or Prefect, Snowflake or BigQuery, Segment, Amplitude, AWS or GCP, Terraform, Looker or Metabase, Fivetran or Airbyte, GitHub Actions, and Kafka (planned).

What You’ll DoPipelines & Warehouse

• Design, build, and maintain scalable ELT/ETL pipelines ingesting data from product surfaces, payment providers, and third-party APIs
• Architect and manage our cloud data warehouse (Snowflake, BigQuery, or Redshift) from the ground up
• Implement orchestration workflows (Airflow, Prefect, or similar) for reliability and full observability

• Optimise warehouse performance and cost through query tuning, clustering, and sensible warehouse sizing

   

Transformation, Quality & Governance

• Develop clean, well-documented, testable transformation layers (dbt or equivalent), applying dimensional modelling principles
• Build and govern a canonical semantic layer so metric and entity definitions stay consistent across every BI tool and consumer
• Define and enforce data quality standards, SLAs, tests, and monitoring across critical datasets, and maintain a central data catalogue with clear lineage

• Build security and governance into the platform: RBAC, PII classification and masking, encryption, and retention and deletion workflows that support privacy requests

   

Product & Engineering Partnership

• Partner with Product and Growth to instrument event tracking (Segment, Amplitude, or similar) and deliver datasets that power funnels, subscription metrics (MRR, churn, LTV), and A/B test reporting
• Collaborate with Software Engineers on data contract design, API logging, and Change Data Capture where needed

• Champion DataOps practices: version control, CI/CD for data, and documentation-as-code

   

What We’re Looking For (Must-Have)

• 3+ years of professional experience as a Data Engineer or in a closely related role
• Strong command of SQL and at least one of Python or Scala for data processing
• Hands-on experience with a modern cloud data warehouse (Snowflake, BigQuery, or Redshift)
• Experience with workflow orchestration tools (Airflow, Prefect, Dagster, or equivalent)
• Prior experience at a B2C SaaS, subscription, or marketplace business, with a grasp of funnels, churn, and LTV
• Solid understanding of data modelling principles and experience with dbt or a similar transformation framework
• Proven ability to design and scale batch and/or streaming data pipelines in production
• A strong sense of data ownership — you treat data as a product and hold a high bar for quality and reliability
• A clear communicator who can translate technical decisions into business value for non-technical stakeholders

• Comfortable handling personal data responsibly, applying access controls and privacy-aware practices

   

Nice to Have

• Familiarity with product analytics tools such as Segment, Amplitude, or Mixpanel
• Experience building or contributing to a semantic layer or canonical data model
• Experience with streaming platforms (Kafka, Kinesis) for real-time use cases
• Exposure to ML infrastructure or feature stores, a plus as we expand into AI products
• Experience setting up a data platform from scratch in a scale-up environment

Working Conditions

• Competitive compensation
• Remote-first culture with flexible working hours 
• 22 paid vacation days + local national holidays 
• Opportunity to shape the excellence across a growing engineering organisation 
• Modern stack, scalable products, and meaningful technical challenges 

About the project  

We are building a new Data Platform that consolidates data from various third-party SaaS products into centralized analytical environments such as Snowflake, Databricks, and Microsoft Fabric. The platform will provide clients with secure access to reporting and analytics-ready data through Data Warehouses and APIs. 
This is a greenfield initiative where the selected engineer will play a key role in defining architecture, selecting technologies, building ETL/ELT pipelines, and establishing the foundation for a long-term Data Platform offering. 

Requirements:  

• 5+ years of experience in Data Engineering or Data Architecture 
• Strong commercial experience with Snowflake and/or Databricks or Microsoft Fabric 
• Experience designing and building Data Warehouse or Data Lake solutions 
• Hands-on experience with Big Data processing 
• Strong ETL/ELT pipeline development experience 
• Excellent SQL skills 
• Experience working with large-scale datasets (terabytes of data) 
• Strong understanding of data modeling and performance optimization 
• Upper-Intermediate+ English 
• Ability to work independently and make architecture decisions 

 
Responsibilities: 

• Design and implement scalable Data Warehouse and Data Lake architectures 
• Build and optimize ETL/ELT pipelines 
• Develop data ingestion solutions for multiple third-party SaaS platforms 
• Process and transform large volumes of structured data 
• Design incremental data synchronization processes 
• Evaluate and introduce modern Data Engineering tools and technologies 
• Optimize platform performance, scalability, and cost efficiency 
• Collaborate with stakeholders to define the future architecture of the platform 
• Ensure high-quality, analytics-ready data for reporting and downstream consumer 

Schedule: Flexible Part-Time (approx. 20–25 hours per week). Agile schedule with the ability to balance daily workload (4–6 hours/day).No late meetings and overtime.   

Why Choose Us? What Do We Offer that's Exceptional?    

• Greenfield architecture with no legacy constraints 
• Opportunity to build a modern Data Platform from scratch 
• Freedom to influence technology choices and architectural decisions 
• Challenging Big Data and distributed data processing problems 
• High level of ownership and autonomy 
• Direct impact on a new strategic company initiative 
• Work with the latest technologies in the Data Engineering ecosystem 

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.

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

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 are looking for a Senior Data Engineer with strong hands-on experience in Apache Spark, PySpark and GCP Dataproc to join Implex and work on a long-term data platform project for a public-sector in the Middle East.

The engineer will contribute to the development and support of data-processing pipelines that integrate multiple enterprise data sources, including PostgreSQL, SAP HANA and S3-compatible object storage. The role involves not only writing PySpark jobs but also troubleshooting distributed workloads, configuring Dataproc environments, securing external connections and ensuring reliable delivery of data into the target data warehouse.

Responsibilities

• Develop, maintain, and optimize ETL pipelines using Apache Spark and PySpark.
• Configure, run, and troubleshoot Spark workloads on GCP Dataproc.
• Package and submit Spark jobs, manage dependencies, and analyze driver and executor logs.
• Identify and resolve performance issues related to memory usage, shuffling, partitioning, data skew, and distributed data processing.
• Integrate Spark workloads with S3-compatible object storage using the Hadoop S3A connector.
• Configure and troubleshoot Spark connectivity with MinIO, including bucket policies, custom endpoints, path-style access, TLS, signature compatibility, and redirect handling.
• Configure custom CA certificates and Java truststores for Spark drivers and executors.
• Implement and optimize scalable Spark JDBC reads and writes for PostgreSQL and SAP HANA, including partitioning, batching, pushdown, and data type mapping.
• Build reliable incremental data loads, retries, backfills, idempotent processing, and data reconciliation mechanisms.
• Design and support staging-to-publish data flows, bulk data loads, and data warehouse integration patterns.
• Contribute to data modeling, schema evolution, Slowly Changing Dimension patterns, data lineage, technical documentation, and data quality practices.
• Apply secure secrets management and least-privilege access principles across data integrations.

Must-Have Technical Skills

• Apache Spark / PySpark development (Dataproc): driver/executor behavior, job packaging/submission, performance tuning
• GCP Dataproc operations: cluster configuration, init actions, dependency management, troubleshooting via logs/metrics
• Hadoop S3A connector: `fs.s3a.*` configuration, endpoint/path-style access, credential providers, S3 semantics
• MinIO (S3-compatible) integration: bucket policies, TLS endpoints, signature/redirect troubleshooting
• TLS/SSL & PKI with custom CA: certificate chains, SAN/hostname validation, diagnosing handshake/PKIX errors
• Java truststores (JKS/PKCS12) & JVM SSL config: `keytool`, distributing truststores, setting driver/executor JVM options
• PostgreSQL integration: Spark JDBC reads/writes at scale, indexing/performance basics, data type mapping
• SAP HANA integration: JDBC/ODBC connectivity, driver management, calculation views vs tables, pushdown/performance tuning
• ETL engineering: incremental loads/CDC concepts, idempotency, retries, backfills, data quality/reconciliation
• Data Warehousing integration: strong SQL, staging-to-publish patterns, SCD concepts, bulk load strategies
• Data modeling & governance basics: dimensional modeling, schema evolution, lineage/documentation practices

Nice-to-Have Skills (but not must)

• Linux + networking fundamentals: DNS, routing, firewall/LB/proxy basics; tools like `curl`/`openssl s_client` for validation
• Secure secrets handling: GCP Secret Manager (or equivalent), least-privilege access, avoiding hardcoded credentials
• KAFKA knowledge if we ever bring KAFKA into the architecture again

Hiring process

• HR interview
• PM/Technical interview with Implex
• Dev Lead interview on the client side

What we offer

• A long-term international project
• Opportunity to work on a national-scale digital platform used by thousands of users
• Remote full-time collaboration
• Professional and supportive team environment
• Challenging technical tasks and a meaningful product with real-world impact

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
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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”

We are looking for a Senior Data Engineer to join our team. This is not a standard ETL role. We are building a dynamic data ecosystem where AI is deeply integrated - both as a productivity multiplier and as a core component of our data processing logic for identity data enrichment and data scoring.

You will own the infrastructure that handles over 10,000 executions per minute, ensuring stability, scalability, and data integrity. You will work with a modern stack on Google Cloud Platform, utilizing Cloud Functions and Kubernetes.

We are looking for an engineer who improves infrastructure, automates everything, and is eager to implement LLM-based logic directly into high-load data flows.

Responsibilities

• AI-Driven Data Scoring: Design and implement pipelines that utilize LLMs to analyze and score identity data in real-time. You will integrate AI models directly into the decision-making loop, balancing accuracy with latency and cost.
• Own the Data Architecture: Architect scalable data solutions using GCP and Python. You will manage data storage and retrieval using BigQuery and Apache Iceberg to support querying of TBs of data.
• Heavy Data Processing: Utilize Apache Spark for data transformations and batch processing when lightweight cloud functions are not enough.
• Manage High-Load Orchestration: Maintain and optimize our system instances. It involves complex dataflows, custom Python nodes, and performance tuning for 10k+ execs/minute.
• Release Lifecycle (CI/CD): Take ownership of the deployment process, ensuring that updates to pipelines and infrastructure are released safely with proper testing and rollback strategies.
• Database Optimization: Manage PostgreSQL performance under heavy load, optimizing complex queries and indexing strategies.
• Active AI Usage: Use Claude Code and other AI engineering tools to accelerate your own development, refactoring, and testing processes.
• Incident Resolution: Proactively monitor the system. When alerts fire, you investigate the root cause—whether it’s a database lock or an LLM hallucination—and fix it permanently.
   

Requirements

• 4-5+ years of experience in Data Engineering or Backend Engineering with a strong data focus.
• Production AI Integration: Experience integrating LLMs (OpenAI, Anthropic, Gemini) into production applications via API. You understand latency, token limits, and how to structure data for AI scoring.
• Expertise in GCP: Understanding of Google Cloud Platform (Cloud Functions, IAM, Networking).
• Strong Python: You write clean, efficient, and testable code. You are comfortable building custom logic where standard tools fall short.
• Big Data Stack: Experience with BigQuery, Apache Spark, and modern table formats like Apache Iceberg.
• Kubernetes (K8s): Experience deploying and scaling services in containerized environments.
• Workflow Automation: Understanding of workflow orchestration tools at a deep technical level. N8N is a big part of our domain, so familiarity with it is highly valuable.
• PostgreSQL Mastery: Proven ability to handle heavy write/read loads and optimize schemas.
• English: B2+ (Upper-Intermediate) or higher.
   

Culture & Mindset

• Self-improvement: You are a fast learner. You don't fear AI replacing you; you master it to replace your manual tasks.
• Ownership: You treat the Identity domain as your own business. If a scoring model drifts or a pipeline slows down, you notice it and fix it without being asked.
• Internal Locus of Control: You take responsibility for outcomes. If an external API fails, you build a fallback mechanism instead of just blaming the provider.
• Get It Done: You prioritize shipping value. You know when to use a simple script and when to build a complex architecture.

Openness: You share knowledge freely. If you find a better way to prompt the AI for scoring, you share it with the team.

Cargofy is building the AI-native operating system for global freight. Cargofy deploys autonomous AI agents that automate procurement, dispatching, negotiations, operations, tracking, and workflows for carriers, shippers, and 3PLs across the US, Europe, and Asia. Our platform processes 100,000+ loads per day and hundreds of thousands of real-time events — replacing manual logistics with fully autonomous AI.

We need an Integration Engineer (PHP/Laravel) to build and maintain the connectors that plug our AI agents into the systems logistics runs on — load boards, TMS platforms, telematics, finance, e-sign, and communication services. The catalog is large and growing constantly: there will always be a next connector.

Why This Role Matters

Our AI agents are only as capable as the systems they can reach. An agent that finds loads needs load boards like DAT. An agent that negotiates needs telephony and email. An agent that files documents needs e-sign. Every connector you ship gives every agent on the platform a new capability — and every customer gets it within days.

What You Will Do

• Build and maintain integrations with third-party systems: load boards, TMS and ERP platforms, telematics/ELD providers, LTL carriers, finance and e-sign services, messaging and telephony
• Design connectors that survive the real world: rate limits, flaky third-party uptime, breaking API changes, retries/backoff, idempotency
• Build two-way sync flows — loads in, statuses and documents back out — where the customer's system stays the system of record
• Wrap legacy APIs (SOAP/XML, government e-services) into clean internal interfaces our agents consume
• Own connector health: monitoring, alerting, and recovery for integrations processing events at scale

Tech Stack

• PHP 8.x — strong OOP, PSR standards, clean code
• Laravel — queues (Redis + Horizon), jobs, events, service container
• REST, OAuth2, webhooks; SOAP/XML for legacy APIs
• PostgreSQL, event-driven architecture
• PHPUnit, CI/CD pipelines, Docker

Who You Are

• 3+ years of production PHP/Laravel experience, with real third-party API integration work behind you
• You treat other people's APIs as a hostile environment — you design for failure, not the happy path
• You can read undocumented or badly documented APIs without waiting for support tickets
• Independent and proactive — a new connector is a project you scope and ship, not a task you receive
• Logistics domain experience (TMS, load boards, ELD) is a strong plus, not a requirement

Why Cargofy

• Your connector ships to production within days and immediately powers AI agents used by thousands of logistics companies
• Work at the frontier of applied AI — the integrations you build are what lets our agents make real phone calls, book real loads, and file real documents
• Competitive compensation + stock options for top performers
• Clear OKR system, strong engineering culture, and a team that ships daily
• Remote-first with offices in Kyiv, Warsaw, and Houston