Jobs Data Engineer

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.

Are you passionate about building scalable, reliable data systems that power real-world solutions? We are looking for a Senior Data Engineer to join our team and work remotely from Europe, Ukraine, or LATAM.

At Sigma Software, we partner with innovative companies to deliver cutting-edge technology solutions. This role offers the opportunity to design and operate a canonical data platform that integrates diverse external data sources, ensuring high quality and flexibility.

Why join us? You’ll work with modern technologies, collaborate with talented professionals across multiple regions, and have the freedom to experiment and innovate while contributing to impactful projects in the AdTech domain.

CUSTOMER

Our client is a fast-growing AdTech company focused on solving real-world challenges in digital advertising and data-driven marketing. They specialize in building highly resilient and scalable systems that encourage experimentation and innovation. Their data platform is designed to be both powerful and flexible, enabling engineers to introduce new ideas and leverage the latest technologies to optimize advertising performance and audience insights.

PROJECT

The project involves developing and maintaining a canonical data platform that integrates multiple external data sources. It supports both full data loads and incremental updates, ensures high data quality, and leverages AI-assisted tools for monitoring and issue detection.

Key Technologies: Java, Scala, SQL, GCP Dataflow, REST APIs, AI-assisted tools, event-driven architectures

RESPONSIBILITIES

• Design, build, and maintain reliable data pipelines to ingest and process data from external systems
• Implement integrations using Scala, ensuring scalability and resilience
• Handle both full data loads and incremental updates efficiently
• Collaborate with engineering and product teams to align on integration and data needs
• Develop and maintain a unified internal data model
• Ensure data quality, detect and resolve incorrect or incomplete data
• Utilize AI-assisted tools to improve data quality and operational efficiency
• Monitor and optimize data pipelines for production readiness

QUALIFICATIONS

• At least 5 years of experience as a Data Engineer or Backend/Data Engineer
• Strong production experience with Scala
• Proven experience integrating external systems via REST APIs (pagination, rate limiting, token-based authentication)
• Strong data modelling skills with structured, relational data
• Solid SQL knowledge
• Experience with GCP Dataflow
• Experience operating data pipelines in production environments
• Strong communication skills and ability to collaborate across teams
• At least an Upper Intermediate level of English

WILL BE A PLUS

• Experience with event-driven architectures or messaging systems
• Designing canonical schemas across multiple data sources
• Experience with LLM (Large Language Models)

PERSONAL PROFILE

• Proactive and detail-oriented
• Thrives in collaborative environments
• Enjoys solving complex integration challenges
• Comfortable experimenting with new technologies
• Committed to improving data systems

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”

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

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

MacPaw is a software company that develops and distributes software for macOS and iOS. Today, we have 20 million active users across all our products, and every 5th Mac on Earth has our product installed.

At MacPaw, we believe humans and technology can reach their greatest potential together.

We gather open-minded people who support each other and aspire to change the world around us, making millions of people’s lives easier with technology.

Our flagship products include CleanMyMac, Setapp, ClearVPN, Moonlock, and more. Now, we’re stepping into an important new stage: launching a product ecosystem with a new AI Mac Assistant — Eney.

We are expanding our team and looking for a Data Engineer to help us build a scalable, reliable data architecture.

In this role, you will build reliable data pipelines, design clear domain models, and optimize our data warehouse. We value a practical, problem-solving mindset — someone who easily navigates ambiguous requirements, brings structure to complex tasks, and takes ownership of their solutions.

In this role, you will:

• Build and support Airflow pipelines to ingest data from third-party sources (APIs, payment providers, ad networks) with retries, backfills, and quality checks.
• Work directly with Analytics Engineers and product teams to translate ambiguous business requests into reliable data models.
• Design and implement fact and dimension tables in dbt using dimensional modeling principles.
• Migrate existing SQL transformations into dbt models, adding tests and documentation.
• Refactor the current dbt project: improve layer structure, standardize naming conventions, and set up CI checks.
• Optimize slow or costly warehouse queries and materializations.
• Evaluate table formats and set up initial ingestion flows for our Data Lakehouse prototype.

Skills you’ll need to bring:

• Strong proficiency in Python and advanced SQL, including complex transformations, window functions, and query optimization.
• Proven experience building and orchestrating ELT/ETL pipelines using Airflow.
• Solid experience with dbt for data modeling, testing, documentation, and managing project structure.
• Good understanding of dimensional modeling concepts, Kimball methodology, star/snowflake schemas, and SCDs.
• Practical experience working with cloud data warehouses (preferably BigQuery, Redshift, or Snowflake) and Cloud Storage.
• Hands-on experience with Docker, Git workflows, and basic cloud/containerized environments.
• A problem-solving mindset with the ability to handle ambiguous requirements, communicate effectively with stakeholders, and use AI/LLM tools (like Claude) to speed up delivery.
• At least an Intermediate level of English and fluent Ukrainian.

As a plus:

• Experience with change data capture (CDC) tools and patterns (e.g., Debezium).
• Background in building and optimizing large-scale data processing jobs with PySpark.
• Experience with real-time data processing using message brokers like Kafka or RabbitMQ.
• Experience with stream processing frameworks (Flink, Kafka Streams).

What we offer:

• We are a Ukrainian company, and we stand with Ukraine against the russian aggression
  We maintain workplaces for the mobilized Macpawians and provide financial support to colleagues or their families affected by the war. Here, you can also read about the MacPaw Foundation, which intends to help save the lives of Ukrainian defenders and provide relief to as many civilians as possible.
• We are committed to our veterans
  Our Veteran Career and Empowerment Program is designed to ensure our veterans and active military personnel receive the recognition, support, and opportunities they deserve.
• Hybrid work model
  Whether to work remotely or at the hub is entirely up to you. If you decide to mix it, our Kyiv office, which works as a coworking space, is open around the clock. The office is supplied with UPS and Starlink for an uninterrupted work process.
• Your health always comes first
  We guarantee medical insurance starting on your first working month. For those abroad, you can receive a yearly Medical insurance allowance as compensation for managing your medical expenses.
• Flexible working hours
  You can choose a schedule that is comfortable for you. No one here tracks your clock in/out because MacPaw is built on trust and cooperation.
• Space to grow both professionally and personally
  Education opportunities to grow both hard and soft skills, annual development reviews, and internal community.
• Teams we are proud of
  We build honest, transparent, and reliable relationships within teams. Every Macpawian can improve processes and implement their ideas. We encourage open and constructive feedback and provide training for Macpawians on giving and receiving feedback.
• Office designed for people (and pets)
  Our office has it all: a spacious workplace with enough room for sitting up, lying down, and running around; a gym for recreation; cozy kitchens; a sleeping/meditation room; and a terrace with a view where we throw summer parties. Also, we have two cats living in the office, and you are welcome to bring your pets to the office (we have separate floors for cats and dogs).
• Time-off policy that covers life’s needs
  Convenient personal time-off policy to help you take care of essential matters in your personal life, and parental leaves. On top of all that, sabbaticals are open after 5 years of being with MacPaw.
• Join social initiatives with MacPawCares
  MacPaw participates in numerous humanitarian aid and charity projects across many fields, and you are welcome to jump in to make the world a better place.
• We’re an equal-opportunity employer. Here is a safe place for applicants of all backgrounds
  We are hiring talented humans. Meaning with all our variety of backgrounds and identities, including service members and veterans, women, members of the LGBTQIA+ community, individuals with disabilities, and other often underrepresented groups. MacPaw does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
  
  *Some benefits are under development, and new adjustments are possible.

Who are we?

Fuelfinance is on a mission to revolutionize financial management for early-stage startups, empowering them to take control of their finances and thrive. We recently raised $1M in Seed Funding from top-tier investors like Markus Villig (Bolt), John S. Kim (SendBird), Stratmind, and Bad Ideas funds, following 3 years of profitable, bootstrapped success. Trusted by businesses with over $200M in P&L, including Reface, Petcube, and Hampton, we’ve built a universal tool that brings clarity and insight to finance management.

With 38% of startups failing due to cash flow issues, our goal is to prevent these failures and unlock trillions in GDP growth. At Fuelfinance, we’re not just creating software — we’re safeguarding the future of thousands of startups. Join us and be part of this impact!

fuelfinance.me

www.instagram.com/fuelfinance

youtu.be/eI5VH2Ks9o4

Who are we searching for?

We're looking for an Enterprise Finance Engineer — someone who can design and implement AI-powered finance systems for our largest and most complex customers. This role sits at the intersection of Finance, Accounting, Systems, Automation, Integrations, and AI.

You'll work directly with CFOs, Finance Directors, Controllers, IT teams, and finance teams to redesign finance operations through AI, automation, and system integrations. You'll own a small number of large, strategic customers end-to-end — from understanding business processes to designing scalable solutions and continuously improving them as the business evolves.

This isn't a role for someone who wants repeatable implementations or well-defined requirements. Every customer is different. You'll navigate ambiguity, work with demanding stakeholders, and solve unique technical problems without predefined playbooks. If that energizes you, we'd love to meet you.

What are your responsibilities?

• Design and implement AI-powered finance systems for enterprise customers
• Lead discovery sessions to understand finance processes and identify automation opportunities
• Build workflows for reporting, forecasting, financial controls, reconciliations, and planning
• Design integrations across ERP, accounting, CRM, payment, BI, and data platforms
• Translate finance requirements into scalable workflows, automations, and system architecture
• Build AI agents, validation logic, and automation frameworks that reduce manual work
• Partner with Engineering on APIs, integrations, and workflow orchestration
• Coordinate across Engineering, Product, and Customer Success as the trusted technical partner for your customers
• Continuously improve customer solutions as business needs evolve

What are the preferred requirements for the role?

• 10+ years implementing finance systems, automations, or enterprise finance operations
• 1+ year building AI-powered automations, agents, workflows, or internal AI tools
• Hands-on experience with APIs, system integrations, automation platforms, and data warehouses
• Experience working with enterprise or multi-entity organizations (multiple stakeholders, legacy systems, changing requirements, long implementation cycles)
• Proven experience leading implementation projects end-to-end — discovery, solution design, rollout, training, and support
• Fluent English
• Strong understanding of finance and accounting processes, including financial close, reporting, FP&A, revenue, AP/AR, and financial controls
• Ability to understand how financial data flows across systems and departments
• Experience with ERP, accounting, or billing systems
• Proven impact in numbers: hours saved, manual work reduced, faster reporting, or fewer errors
• Excellent communication skills and experience working directly with senior finance stakeholders
• Strong systems thinking — the ability to see the bigger picture and break complex finance processes into scalable, maintainable, reusable solutions
• Ownership: taking full responsibility from problem discovery through implementation and post-launch optimization
• Learning agility: quickly mastering new AI tools, technologies, and approaches
• Strong problem-solving under ambiguity, with the judgment to make trade-offs and prioritize
• Strong drive to eliminate repetitive manual work
• Proactivity and a high level of responsibility
• Ability to work in a fast-paced environment

Will be a plus:

• Python or other scripting experience
• SQL and the ability to retrieve and validate financial data independently
• Experience with tools like NetSuite, QuickBooks, Xero, Stripe, Salesforce, HubSpot, Zuora, BigQuery, Snowflake, PostgreSQL, Looker, Power BI, Tableau, Zapier, Make, n8n, or Workato
• Understanding of enterprise architecture — security, scalability, governance, and integration patterns
• Experience working with US-based clients or startups

Reporting Structure: reporting to the Head of FP&A

Recruitment Process:

• Soft skills interview with the recruiter
• Test task
• Hard skills interview with the Head of FP&A
• Culture fit interview with the Head of FP&A/VP of Operations

Why work with us?

• Build the future of finance work — design AI-native finance operations, not just maintain them
• Rapid skills improvement (you'll work with multiple client businesses with different models and challenges)
• Growth opportunities according to our seniority grading (it comes with a compensation increase)
• Flexible schedule and time-off policy
• 18 days of paid vacation per year, paid sick leaves

Job Description 

We are seeking a Multimedia Engineer for a long-term, full-time, remote project to join our engineering team, working on advanced audio/video streaming solutions for embedded platforms. The ideal candidate will have strong hands-on experience with GStreamer and a deep understanding of multimedia systems, media pipelines, and streaming technologies. 

Technical Requirements 

• 5+ years of software development experience in multimedia or system-level environments 
• Strong hands-on experience with GStreamer at the developer level (must-have), including: 

     - ​ building and debugging complex media pipelines 

     - working with elements, pads, caps, and plugins 

     - diagnosing latency, synchronization, and performance issues 

• Strong programming skills in C++ and Java for system-level or multimedia applications 
• Solid understanding of audio and video streaming protocols (RTP, RTSP, etc.) 
• Experience working with video and audio codecs (H.264, H.265, VP9, AAC, Opus, etc.) 
• Experience with Video4Linux (V4L2) Expe .;
• Experience with embedded platforms (Embedded Linux and/or Embedded Android) 
• Understanding of networking fundamentals: TCP, UDP, HTTP 
• Ability to debug complex issues across software layers (middleware, OS, hardware interaction) 
• Experience with version control systems (Git).
• Excellent problem-solving, critical thinking, and communication skills 

Nice to Have 

• Experience with Embedded Android / Android BSP (AOSP) 
• Familiarity with multimedia and graphics systems such as: 
• Android Audio / SurfaceFlinger 
• ffmpeg, VAAPI, OpenMAX, OpenGL 
• Experience with streaming technologies such as WebRTC 
• Experience with camera integration or camera subsystem customization 
• Knowledge of hardware-accelerated media pipelines 
• Scripting experience (Bash, Python) 

Responsibilities 

• Design, develop, and maintain multimedia middleware components 
• Build, integrate, and optimize GStreamer-based audio/video pipelines.
• Integrate media components with user-space applications and system services 
• Debug and resolve complex media issues related to performance, stability, and synchronization 
• Drive technical discussions and contribute to architecture decisions 
• Collaborate with hardware, platform, and application teams 
• Support board bring-up and hardware validation related to audio/video subsystems 

About the Client:

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role:

We are looking for a Middle-level Software Engineer to work on the core query engine of a large-scale distributed data platform. You will develop features across query planning, optimization, and execution, contribute to performance-critical components, and help evolve the platform's ability to process massive analytical workloads across heterogeneous data sources.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities:

• Develop and maintain features across the query engine — planning, optimization, execution, and data access layers.
• Write high-quality, performance-conscious code in Java and/or C++.
• Work with SQL semantics, query plans, and execution operators over large-scale distributed data.
• Integrate with columnar formats, open table formats, and connectivity drivers used across the platform.
• Contribute to CI/CD pipelines and automated testing — participate in build, test, and release workflows in Jenkins.
• Deploy and validate work in Kubernetes (GKE / EKS / AKS) across GCP, AWS, and Azure using Docker.
• Debug complex issues spanning query planning, distributed execution, memory management, and I/O.
• Collaborate closely with US-based engineering teams on design reviews, code reviews, and technical decisions.

Required Qualifications:

• Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field.
• Programming: Strong proficiency in Java or C++, with solid OOP and software design fundamentals. Candidates comfortable with both are especially valued.
• SQL & Data: Strong SQL skills and solid understanding of relational and analytical data systems, including query execution concepts.
• Data processing systems experience: Hands-on experience with data processing systems — query engines, distributed databases, ETL/ELT frameworks, or analytical platforms.
• Engineering experience: 3+ years in backend/systems software engineering.
• CI/CD & DevOps basics: Practical experience with Jenkins pipelines and modern development workflows.
• Containers: Working knowledge of Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency).
• Cloud: Comfortable operating in at least one major cloud (GCP, AWS, or Azure).
• Version Control: Confident with Git / GitHub workflows.
• English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team.
• Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team.

Desired Skills:

• Experience with Apache Arrow (columnar in-memory format).
• Familiarity with Apache Calcite (SQL parsing, planning, optimization framework).
• Exposure to Gandiva or LLVM-based expression compilation.
• Experience with open table formats — Apache Iceberg, Delta Lake, or Hudi.
• Familiarity with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar).
• Understanding of query planning, optimization, and execution internals.
• Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight.
• Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure.
• IaC tools such as Terraform.
• Experience with performance profiling and optimization of latency-sensitive systems.

About the Client:

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role:

We are looking for a Senior Software Engineer to design, build, and own significant components of the core query engine of a large-scale distributed data platform. You will lead work across query planning, optimization, and execution — driving architectural decisions, mentoring middle engineers, and pushing the platform's performance and scalability boundaries across all major clouds.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities:

• Architect, design, and implement significant components of the query engine — planner, optimizer, execution operators, memory management, and data access layers.
• Write and review high-quality, performance-critical code in Java and/or C++.
• Own end-to-end delivery of features from technical design through production rollout.
• Drive performance optimization work — profiling, benchmarking, and eliminating bottlenecks in latency- and throughput-sensitive paths.
• Integrate deeply with columnar formats, open table formats, and connectivity drivers.
• Mentor middle engineers, lead code reviews, and set technical standards for the team.
• Contribute to CI/CD architecture and quality processes across Jenkins, containerization, and multi-cloud deployment (GCP, AWS, Azure via Kubernetes / Docker).
• Debug complex, cross-layer issues spanning query planning, distributed execution, memory management, and I/O.
• Partner with US-based engineering leads on design reviews, architectural decisions, and roadmap execution.

Required Qualifications:

• Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field.
• Programming: Strong proficiency in Java or C++, with deep understanding of OOP, systems design, memory management, and concurrency. Candidates strong in both are especially valued.
• SQL & Data: Advanced SQL skills and strong understanding of query execution internals — planning, optimization, and operator implementation.
• Data processing systems experience: Demonstrated experience building or extending data processing systems — query engines, distributed databases, ETL/ELT frameworks, or analytical platforms.
• Engineering experience: 5+ years in backend/systems software engineering, with a track record of owning significant components.
• CI/CD & DevOps: Solid experience with Jenkins and modern engineering workflows.
• Containers & Orchestration: Strong working knowledge of Docker and Kubernetes.
• Cloud: Hands-on experience with at least one major cloud (GCP, AWS, or Azure); exposure to more than one is a strong plus.
• Version Control: Confident with Git / GitHub workflows and rigorous code review practices.
• English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team.
• Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team.
• Leadership: Prior experience mentoring engineers, leading technical designs, or owning components end-to-end.

Desired Skills:

• Deep experience with Apache Arrow (columnar in-memory format).
• Hands-on experience with Apache Calcite (SQL parsing, planning, optimization framework).
• Experience with Gandiva or LLVM-based expression compilation and code generation.
• Experience with open table formats — Apache Iceberg, Delta Lake, or Hudi.
• Deep familiarity with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar).
• Deep understanding of query planning, optimization (cost-based, rule-based), and execution internals.
• Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight.
• Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure.
• IaC tools such as Terraform.
• Track record of performance optimization at the systems level — profiling, vectorization, cache/memory-conscious design.
• Prior experience contributing to open-source data infrastructure projects.