Jobs Data Engineer

About the Company

A growth equity investment firm focused on partnering with software and technology-enabled services businesses to accelerate expansion, support strategic initiatives, and build stronger teams. The firm typically works with management teams in growth-stage companies and helps drive both organic growth and acquisition-led expansion.

Role Overview

We are looking for Data Engineer / Analytics Engineer contractors who can embed within portfolio teams to produce reporting cubes and revenue analytics deliverables. Ideal candidates will bring a blend of technical depth, commercial awareness, project management skills, and preferably SaaS domain expertise.

What You'll Be Doing

• Embed within portfolio company data teams as a contractor
• Build and deliver data pipelines from raw source data to clean, validated output tables
• Produce reporting cubes and revenue analytics deliverables
• Reconcile pre- vs. post-migration data to maintain analytical continuity
• Translate business questions into technical implementations

Requirements

• Hands-on Technical Builder — Proficient in Python and SQL. Able to independently architect and deliver data pipelines from raw source data through to clean, validated output tables.
• Commercially Oriented — Ideally 2+ years in a consulting, advisory, or deal-support environment (e.g. Big 4, boutique analytics consultancy). Comfortable translating business questions into technical implementations.
• System & Data Migration Experience — Has worked on ERP, CRM, or billing platform migrations (e.g. Salesforce, NetSuite, Chargebee). Understands data quality pitfalls — duplicate customers, broken historical linkages — and knows how to reconcile pre- vs. post-migration data to maintain analytical continuity.
• Accounting or SaaS Exposure — Either an accountant by trade or someone with SaaS revenue analytics exposure. Familiar with subscription metrics (ARR, NRR, churn), snowballs, and billings-to-revenue reconciliations.

Candidate Requirements

• Ukrainian native speaker
• English proficiency: C1 or higher
• Remote — candidates located anywhere are welcome to apply

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.

🚀 We’re on the hunt for a talented AI/ML Engineer to design, implement, and optimize cutting-edge AI/ML solutions. If you thrive on solving complex problems and creating production-ready models, we’d love to hear from you!

🎯 What You’ll Do:

• Develop and fine-tune AI/ML models for performance and scalability.
• Enhance production models, debug issues, and ensure reliability.
• Work with cross-functional teams to shape future AI/ML innovations.
• Explore emerging AI/ML technologies and recommend tools/frameworks.
• Build robust pipelines using Python, SQL, Docker, and more.

🧩 What We’re Looking For:

• Strong experience in AI/ML, data science, or data engineering.
• Expertise in Python, TensorFlow, Keras, Scikit-learn, and Generative AI.
• Familiarity with Docker, Streamlit, and data tools like Pandas and SQL.
• Strong communication skills and the ability to work independently.
• English: Upper-intermediate or higher.

🎯 We offer:

• Paid vacation and sick leave
• We provide the equipment you need to work comfortably (if required)
• We cover 50% of courses if you want to grow professionally
• Regular salary reviews based on your performance
• Competitive salary, depending on your experience
• Always on-time payments — no delays

• A normal, supportive team without unnecessary bureaucracy

   

Ready to shape the future of AI/ML with us? 🚀 Apply now and be part of something extraordinary!

🚀We are looking for a Middle+/Senior Data Engineer to join a long-term data platform project. The role is focused on building, maintaining, and optimizing scalable data pipelines, data warehouses, and cloud-based data platforms for high-load business environments.

The ideal candidate has strong hands-on experience with Python, SQL, Spark/PySpark, cloud data services, and modern ETL/ELT pipelines. Experience with AWS is highly important, while Databricks, Snowflake, BigQuery, dbt, Airflow, Kafka/Kinesis, and Terraform are strong advantages.

🧩Responsibilities:

• Design, build, and maintain scalable batch and near-real-time data pipelines.
• Develop and optimize ETL/ELT workflows using Python, SQL, Spark/PySpark, Airflow, dbt, or similar tools.
• Work with cloud-based data platforms and services, especially AWS.
• Build and support data lakes, lakehouse architectures, and data warehouses.
• Integrate data from APIs, databases, SaaS tools, files, and streaming/event-based sources.
• Optimize data processing performance, query execution, partitioning, and storage costs.
• Ensure data quality, reliability, monitoring, logging, and validation across pipelines.
• Collaborate with engineers, analysts, BI teams, data scientists, and business stakeholders.
• Support documentation, data modeling, and data dictionary creation where needed.
• Contribute to CI/CD, infrastructure automation, and cloud data platform improvements.

🔑Requirements:

• 3-5 years of experience in Data Engineering or closely related roles.
• Strong hands-on experience with Python, SQL, and Spark/PySpark.
• Experience building and maintaining production-grade ETL/ELT pipelines.
• Strong understanding of data warehouses, data lakes, lakehouse architectures, and data modeling.
• Experience with AWS data services such as S3, Glue, Lambda, Athena, EMR, Redshift, Firehose, Kinesis, SQS, or EventBridge.
• Experience with orchestration and transformation tools such as Airflow, dbt, Prefect, Dagster, or similar.
• Experience with at least one modern data platform: Databricks, Snowflake, BigQuery, Redshift, or similar.
• Understanding of data quality, monitoring, observability, and performance optimization.
• Experience with Git, CI/CD, Docker, Terraform, AWS CDK, or other DevOps/IaC tools.
• Ability to work independently, communicate clearly, and take ownership of delivery.

• English level: Upper-Intermediate or higher.

   

🧩Nice to Have:

• Experience with Kafka, Kinesis, Pub/Sub, or other streaming/event-driven technologies.
• Experience with Delta Lake, Iceberg, Hudi, or other lakehouse table formats.
• Experience with Kubernetes.
• Experience in high-load, fintech, healthcare, telecom, eCommerce, SaaS, or analytics-heavy products.
• Experience with BI/reporting tools such as Tableau, Looker, Metabase, Power BI, or similar.
• Exposure to AI/ML or LLM-related data workflows.
• Experience with GCP or Azure data services.

🎯We Offer: 

• Paid vacation and sick leave
• We provide the equipment you need to work comfortably (if required)
• We cover 50% of courses if you want to grow professionally
• Regular salary reviews based on your performance
• Competitive salary, depending on your experience
• Always on-time payments — no delays
• A normal, supportive team without unnecessary bureaucracy

About the Role

We are looking for a Data Engineer to design and implement scalable data pipelines that support Generative AI applications. You will work with large-scale datasets, optimize performance, and ensure seamless data integration across AI workflows.

Responsibilities:

• Design and develop scalable data pipelines to support AI/ML models.
• Work with structured and unstructured data for training and inference.
• Optimize ETL workflows for real-time and batch processing.
• Ensure data quality, governance, and security in cloud environments.
• Collaborate with AI/ML engineers to streamline data workflows.

• Deploy and manage data infrastructure on AWS, GCP, or Azure.

   

Requirements:

• 4+ years of experience in Data Engineering and Big Data technologies.
• Expertise in ETL pipelines, data warehousing, and distributed computing.
• Strong knowledge of SQL, NoSQL, and data lakes.
• Experience with cloud-based data services (AWS Glue, BigQuery, Azure Data Factory).
• Proficiency in Python, Spark, Kafka, Airflow.
• Experience with CI/CD, DevOps, and Agile methodologies.

What You’ll Get at Uvik:

• Generous paid time off: 18 vacation days + 12 sick leaves annually.
• Top-tier gear: MacBook Pro + Dell Monitor provided.
• Flexibility: manage your schedule, work fully remote, and keep a healthy work-life balance.
• Challenging projects: work with global clients and the latest technologies.
• Growth opportunities: we invest in your development and support career advancement.
• Competitive salary in a fast-growing international company.

Equals5 is a leading Healthcare Marketing SaaS for Pharma and Life Sciences. Our platform leverages exclusive NPI-level targeting technology to help brands reach healthcare professionals across 20+ channels with precise, user-level reporting.

The Role

We are looking for a Senior Data Engineer to join the Identity team at Equals5. 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.

What We Offer

• Fully remote with flexible hours (aligned with EU timezones for syncs).
• AI-Native Environment: We provide licenses for Claude Code and encourage using the bleeding edge of AI tech for both daily coding and product features.
• High-Impact Role: You will directly influence how we identify and score users, impacting the core business logic.
• Cross-functional visibility: Work closely with Product and Tech Leads to shape the future of Identity.

• No Bureaucracy: Fast decisions, no legacy processes, focus on results.

   

Are you passionate about building reliable, scalable data systems? We are looking for a Junior Data Engineer to join our Data Engineering team at Sigma Software. This is a remote, full-time opportunity where you will work on a modern cloud-native data stack, contributing to high-impact projects across the business.

At Sigma Software, we believe in empowering our people to grow, share ideas, and make a difference. You will be part of a supportive, feedback-driven culture with opportunities for skill development and career progression.

Customer

Our Customer is a global provider of comprehensive security solutions designed to protect individuals, organizations, and communities. Operating across multiple sectors, they address vulnerabilities and ensure protection against a wide range of risks, with a strong focus on digital security. Their innovative approach combines advanced technology and proactive strategies to safeguard high-value assets and maintain operational stability.

Project

The project delivers comprehensive visibility and autonomous governance over dynamic execution patterns. By enforcing real-time operational protocols, it safeguards high-value information assets against unauthorized access and proactively neutralizes systemic anomalies to ensure total environment stability. The team works with a modern cloud-native data stack, enabling scalable and reliable data infrastructure across the business.

Key Technologies: Python, Snowflake, BigQuery, dbt, Go, Kafka, Airflow

Requirements

• 2+ years of experience in data engineering
• Experience with Python
• Solid experience with SQL, including writing and debugging queries
• Basic knowledge of dbt
• Familiarity with Snowflake
• Curiosity for learning and willingness to adopt new tools
• Collaborative mindset and strong communication skills
• Attention to detail regarding data quality and validation
• Good organizational skills and ability to manage tasks in a fast-paced environment
• Upper-Intermediate level of English

Will be a plus

• Knowledge/experience with Go, Kafka, Airflow

Personal Profile

• Motivated and detail-oriented professional with a passion for data engineering
• Enjoys learning new tools and technologies

• Committed to delivering high-quality, reliable data solutions

   

  Responsibilities

• Assist the Data Engineering team with migration from BigQuery to Snowflake
• Translate and optimize SQL queries across platforms
• Perform end-to-end validation to ensure data correctness and pipeline reliability
• Write and maintain incremental dbt models
• Collaborate closely with data engineers, analysts, and stakeholders to understand data needs and deliver solutions
• Participate in code reviews, share knowledge, and contribute to team best practices
• Document work clearly to support maintainability and knowledge sharing

We are looking for a Senior Data Engineer to support the buildout of a centralized data platform in Snowflake. This role will focus on designing and implementing scalable data pipelines to consolidate large volumes of data from diverse sources into a unified platform. The emphasis is on high-throughput data engineering delivery, with a strong focus on scalability, reliability, and rapid execution.

Requirements: 

• Structured and semi-structured data 
• Large datasets (high-volume ingestion and processing) 
• Understanding of Medallion architecture and modern data platform design 
• Experience with orchestration tools (e.g., Airflow or similar) 

Advanced (Nice to Have) 

Experience with: 

• DataOps / observability frameworks 
• Metadata management / data catalogs 
• Multi-source data integration 

Familiarity with: 

• Cloud environments (Azure preferred) 
• Distributed query engines (e.g., Trino) 

Soft Skills / Mindset 

• Ability to work in a high-speed delivery environment with many parallel pipelines 
• Comfortable operating in evolving, ambiguous requirements 
• Strong ownership mindset focused on outcomes, not just tasks 
• Effective communication with technical and business stakeholders 

Engagement Model Context 

• Work as part of a scalable, flexible team, supporting ramp-up/down needs 
• Contribute to rapid delivery of multiple pipelines in a compressed timeline 
• Collaborate with a distributed team across regions 

Responsibilities: 

• Design and build end-to-end data pipelines to ingest and process data from: 
• Internal operational and financial systems 
• Multiple commercial real estate data sources 
• Third-party market and macro datasets 
• Develop transformations using DBT to create clean, reliable, and business-ready datasets (Gold layer) 
• Implement and optimize Snowflake-based data models aligned with Medallion architecture (Bronze / Silver / Gold) 
• Deliver pipelines at scale with a focus on: 
• High performance 
• Cost efficiency 
• Maintainability 
• Collaborate with stakeholders to continuously adapt to evolving requirements in an agile environment 
• Ensure data quality, observability, and reliability, including: 
• Data freshness monitoring 
• Pipeline reliability 
• Data validation and QA 
• Contribute to building a modular, loosely coupled data platform architecture to avoid vendor lock-in 
• Strong experience with Snowflake (data modeling, performance tuning, cost optimization) 
• Hands-on experience with DBT for transformation and data modeling 
• Proven track record building large-scale data pipelines 
• Solid SQL expertise and experience with distributed data systems 
• Data Engineering 

What we offer: 

• Annual paid vacation of 18 working days. 
• Extra vacation days for long-lasting cooperation. 
• Annual paid sick leave of 10 days. 
• Maternity/Paternity leave. 
• The opportunity for sabbatical leave. 
• Marriage and Parenthood Package. 
• Compensation for sports activities or health insurance covering (up to 250$ per year) — after the trial period. 
• Career development plan. 
• English and Spanish classes. 
• Paying taxes and managing PE (Private Entrepreneur). 
• Technical equipment. 
• Internal Referral program. 
• Opportunity to take part in company volunteering activities. 
• Sombra is a “Friendly to Veterans” award-holder. 

Strim Duo is a creative digital studio based in Limassol, Cyprus. We specialize in social game development, product engineering, UX/UI design, custom software solutions, mobile apps, and post-launch product support.
Our mission is to build products that work seamlessly across devices, delivering engaging experiences for millions of users worldwide.

We are looking for a strong specialist who goes beyond data processing — someone who understands the business behind the data and can influence decisions through analytics. This role requires autonomy, fast learning, and full ownership of data quality and analytical outputs.

Requirements

• Advanced Python and SQL (query optimization, complex data transformations, large-scale data handling)
• Hands-on experience with ClickHouse or other columnar databases
• Strong background in building ETL/ELT pipelines (ingestion, transformation, orchestration). Experience with Airflow is a plus.
• Solid understanding of Data Warehouse architecture
• Strong Data QA skills (identifying and resolving data issues)
• Experience with Power BI (building and maintaining reports)
• Ability to quickly understand business logic and translate it into data models
• High attention to detail
• English — B2 or higher (working proficiency)

Nice to Have

• Experience with data governance implementation
• Experience in high-load systems
• Experience working with product and business teams

Responsibilities

• Own data quality: identify, analyze, and fix data issues
• Translate business processes into reliable data structures
• Build and optimize ETL pipelines
• Deliver clean and structured data for analytics and BI
• Handle AdHoc analytical requests under tight deadlines
• Maintain and improve Power BI reporting
• Automate repetitive tasks
• Provide analytical support
• Train users on BI tools

What we offer

• Corporate psychologist;
• Gifts for significant occasions;
• Legal support;
• Flexible working hours;
• Direct access to C-level and opportunity to suggest new business ideas and drive changes.

If you are ready to work in a dynamic team, passionate about the product, technology, and the world of entertainment, we’re waiting for your CV and portfolio to schedule an online/offline meeting and get to know each other better!

Your CV will be carefully reviewed by our TA department, and if it meets the requirements of the position, we will contact you within 3 business days to discuss the possibility of scheduling an interview.

We are looking for an Audio Machine Learning / Edge AI Engineer to design and deploy real-time acoustic detection systems operating in complex and noisy environments.

This role focuses on sound-based situational awareness and requires end-to-end ownership - from audio signal processing and ML model development to optimization and deployment on edge devices operating under constrained conditions.

You will work at the intersection of audio signal processing, machine learning, and embedded systems, building robust solutions capable of reliable performance in real-world field environments.

Requirements

• 4+ years in ML / Audio / DSP / Edge AI
• Strong knowledge of audio signal processing (spectrograms, noise reduction, feature extraction)
• Experience working with noisy environments (wind, city, nature)
• Hands-on experience with ML for audio (CNNs, YAMnet, ONNX)
• Proficiency in training on imbalanced datasets and applying augmentation techniques
• Ability to build low-latency streaming pipelines for real-time audio processing
• Experience deploying models on edge devices (Raspberry Pi, Jetson Nano)
• Optimization skills using ONNX, TensorRT, OpenVINO
• Production-level Python engineering experience (clean architecture, multiprocessing, modular pipelines)
• Proven track record of production deployment in real-world scenarios
• Professional proficiency in English

Will be a plus

• Acoustic domain knowledge (drone frequency ranges, Doppler effect, microphone arrays)
• Sensor fusion experience (audio + video, audio + RF detection)
• Hardware integration skills (GPIO, signal triggering)

Personal Profile

• High level of ownership and problem-solving mindset
• Ability to work across R&D, engineering, and hardware constraints
• Comfortable with real-world, non-ideal data
• Strong collaboration skills in cross-functional teams

Responsibilities

• Design and develop audio-based detection and classification systems for challenging real-world environments
• Implement robust signal processing pipelines tailored for noisy outdoor conditions
• Build and optimize machine learning models for sound event detection
• Develop low-latency, high-reliability streaming pipelines
• Handle imbalanced and imperfect datasets using augmentation and synthetic data techniques
• Deploy and optimize models on edge hardware platforms (Jetson, Raspberry Pi, etc.)
• Optimize inference performance using ONNX, TensorRT, and OpenVINO
• Develop production-grade Python systems with modular architecture and multiprocessing capabilities
• Ensure system robustness under variable acoustic conditions and hardware constraints
• Collaborate with ML, hardware, and systems engineering teams to deliver integrated solutions

______________________________________________________________________

Ми шукаємо Audio Machine Learning / Edge AI Engineer, який буде проєктувати та впроваджувати системи акустичного виявлення в режимі реального часу, здатні працювати в складних та шумних середовищах.

Ця роль зосереджена на системах ситуаційної обізнаності, що базуються на звуках, і вимагає повного циклу роботи — від обробки аудіосигналів та розробки ML моделей до оптимізації та розгортання на edge-пристроях, які працюють в обмежених умовах.

Ти працюватимеш на перетині обробки аудіосигналів, машинного навчання та вбудованих систем, створюючи надійні рішення, здатні стабільно працювати в реальних польових умовах.

Вимоги

• 4+ років досвіду в ML / Audio / DSP / Edge AI
• Глибокі знання обробки аудіосигналів (спектрограми, шумозниження, виділення ознак)
• Досвід роботи в шумних середовищах (вітер, місто, природа)
• Практичний досвід роботи з ML для аудіо (CNNs, YAMnet, ONNX)
• Вміння навчати моделі на незбалансованих датасетах та застосовувати техніки аугментації
• Здатність будувати низьколатентні стрімінгові пайплайни для обробки аудіо в реальному часі
• Досвід розгортання моделей на edge-пристроях (Raspberry Pi, Jetson Nano)
• Навички оптимізації з використанням ONNX, TensorRT, OpenVINO
• Досвід інженерії Python на рівні продакшн (чиста архітектура, multiprocessing, модульні пайплайни)
• Підтверджений досвід продакшн-розгортання в реальних сценаріях
• Професійний рівень англійської

Буде плюсом

• Знання акустичної доменної специфіки (частотні діапазони дронів, ефект Доплера, масиви мікрофонів)
• Досвід сенсорного злиття (audio + video, audio + RF detection)
• Навички інтеграції апаратного забезпечення (GPIO, запуск сигналів)

Персональний профіль

• Високий рівень відповідальності та орієнтації на вирішення проблем
• Здатність працювати на стику R&D, інженерії та апаратних обмежень
• Комфортна робота з реальними, неідеальними даними
• Сильні навички співпраці в крос-функціональних командах

Обов’язки

• Проєктувати та розробляти системи виявлення та класифікації звуків для складних реальних середовищ
• Реалізовувати надійні пайплайни обробки сигналів, адаптовані до шумних зовнішніх умов
• Створювати та оптимізувати ML моделі для виявлення звукових подій
• Розробляти низьколатентні стрімінгові пайплайни з високою надійністю
• Працювати з незбалансованими та неповними датасетами, використовуючи аугментацію та синтетичні дані
• Розгортати та оптимізувати моделі на edge-пристроях (Jetson, Raspberry Pi тощо)
• Оптимізувати продуктивність інференсу з використанням ONNX, TensorRT, OpenVINO
• Розробляти Python системи рівня продакшн з модульною архітектурою та multiprocessing
• Забезпечувати стабільну роботу системи в умовах змінної акустики та апаратних обмежень
• Співпрацювати з командами ML, апаратної та системної інженерії для створення інтегрованих рішень

We are looking for an experienced ML Ops & Infrastructure Engineer to join an AI-oriented project focused on building technology solutions for automating complex processes in heavy industry domains — from construction and mining to the energy sector.
 

The team is developing reliable AI and robotics-oriented systems that help people perform difficult and potentially dangerous work more efficiently and safely. The project combines software engineering, AI infrastructure, and real-world hardware-oriented challenges, where reliability, scalability, and the ability to work with high-load ML workflows are essential.
 

In this role, you will work closely with Research and Engineering teams to build infrastructure for data processing, model training, evaluation, and deployment of ML models.
 

Requirements:
— 3+ years of commercial experience in ML infrastructure, MLOps, Data Engineering, or related backend/infrastructure roles.
— Strong experience with AWS and/or GCP.
— Hands-on experience with Docker and Kubernetes (K8s).
— Strong Python skills and solid software engineering fundamentals.
— Experience building and maintaining scalable data pipelines and workflow orchestration systems.
— Experience with orchestration/workflow tools such as Airflow, Argo, Kubeflow, or similar.
— Understanding of distributed compute environments and ML training workflows.
— Experience with Git and bash scripting.
— Strong problem-solving skills and the ability to work independently in a dynamic environment.
— Upper-Intermediate or higher English proficiency.

Nice to have:
— Experience with Terraform or other Infrastructure as Code tools.
— Familiarity with distributed training frameworks (PyTorch DDP, Ray, Horovod, etc.).
— Experience with model monitoring, observability, or data drift detection.
— Experience working with large volumes of unstructured data (video, sensor data, spatial data).
— Background in AI-heavy domains such as computer vision, autonomous systems, robotics, or similar fields.

Responsibilities:
— Design, build, and maintain scalable ML infrastructure and internal platforms.
— Develop reliable data ingestion, processing, and versioning pipelines.
— Build and optimize environments for distributed model training and evaluation.
— Manage cloud compute workloads across AWS and/or GCP.
— Containerize and orchestrate ML workloads using Docker and Kubernetes.
— Collaborate closely with ML researchers and software engineers to improve development workflows.
— Improve infrastructure reliability, scalability, and deployment processes.
— Build tooling and systems that accelerate AI research and production readiness.

Interview stages:
— HR interview (30 minutes).
— Technical interview (1 hour).
— Interview with the customer (1 hour).
— Job offer 🎉🎉🎉

We offer:
— Competitive salary.
— Challenging tasks and projects.
— Opportunities for professional development and growth.
— Flexible working hours.
— New hardware.
— Free English lessons.
— Table tennis and yoga classes.
— Availability of generators and Starlinks in the office.
— Small gym in the office.
— Adjustable height tables.
— Regular office fruit delivery and other benefits.

Let’s create value together!

External Engineer — Google Data API / DV360 Integration

We are looking for an experienced external engineer to help build a new integration with Google’s data APIs, replacing our existing DV360 integration.

The work will involve designing and implementing a production-grade integration with Google’s APIs, including authentication, API request/response handling, data ingestion, error handling, retries, observability, and integration into our existing backend services. The engineer will work with our internal team to understand the current DV360 implementation, map the new Google API capabilities to existing product flows, and deliver a clean, maintainable replacement.

Required experience

• Strong hands-on experience with Clojure
• Experience building backend integrations with external APIs
• Ability to read API documentation, clarify edge cases, and independently drive implementation
• Experience with OAuth / service-account-based authentication, pagination, rate limits, retries, and API error handling
• Strong production engineering practices: testing, logging, monitoring, and maintainable code

Advantage

• Experience with Google Marketing Platform, DV360, Bid Manager API, Google Ads, or similar advertising / marketing-channel APIs
• Familiarity with reporting, campaign, audience, or measurement data flows
• Experience replacing or migrating legacy third-party API integrations

Engagement scope

• Review the current DV360 integration and identify required migration points
• Implement the new Google data API integration in Clojure
• Support testing, validation, and rollout with the internal AppsFlyer team
• Document key implementation decisions, API assumptions, and operational considerations

CodeGeeks Solutions is a software company led by top-notch experts with over 10 years of experience in product design & software development. We are able to comprehensively cover the needs of our clients and focus on the full cycle of development.

Now we are looking for two Data Engineers — one at Middle level and one at Senior level — to join our team and work on a cloud-native Data Lakehouse platform built on AWS!

Why us?

• We trust our team and care deeply that everyone feels comfortable and supported in their work and our positive atmosphere
• This work will be fulfilling! You’ll develop and acquire new skills, and we’ll support you every step of the way
• Here you will find like-minded people and true friends
• Here, you’ll even look forward to Mondays!
• We’re a young and successful team! :)

We Offer

• 15 days of annual time off paid + company-wide New Year holiday in the last week of the year (5 days);
• 20 days of sick leave paid (5 — 100%, 15 — 50%);
• Legal support;
• IT Cluster membership;
• Flexible work schedule and remote work options;
• Chance to make a real impact and shape the future of our company;
• Education compensation;
• Clear career map, continued professional development and growth opportunities;
• Opportunity to work with a talented and dynamic team of experts;
• Internal training and meetups;
• Access to corporate learning materials;
• High-level compensation and regular performance-based salary and career development reviews;
• PE accounting and support;
• Work in a comfortable, modern and cozy office in Lviv;
• Team buildings & corporate events;
• Referral program.

About the Project

You will be part of a team building and scaling a modern data platform that serves BI needs across Finance, Sales, Marketing, Product, and Operations — and lays the groundwork for future AI/ML initiatives. The stack is current, the problems are real, and the business impact is visible from day one.

What You Will Do

• Design and build ELT pipelines from diverse sources: SaaS products, internal systems, and third-party integrations (Twilio, Mixpanel, Salesforce, Google Analytics, SharePoint/Excel)
• Implement and maintain medallion architecture (raw → refined → consumption) using Apache Iceberg
• Work with Airflow and/or Airbyte for pipeline orchestration, scheduling, and operational monitoring
• Support near-real-time and lower-latency data processing where required
• Build data quality checks, automated assertions, lineage documentation, and dataset certification routines
• Apply least-privilege access patterns, dataset classification, auditability controls, and data minimization practices
• Collaborate closely with BI/Data Analysts to ensure schemas support dashboard and reporting needs

• Write runbooks and handover documentation to keep the platform maintainable long-term

   

What We Are Looking For

Middle (2–3 years of experience)

• Solid hands-on SQL, data modeling, and performance optimization skills for analytics consumption layers
• Practical experience building ELT/ETL pipelines
• Experience with AWS services: S3, Glue, Athena
• Experience with Airflow and/or Airbyte
• Experience with Apache Spark / PySpark
• Familiarity with integrating third-party SaaS sources (Twilio, Mixpanel, Salesforce, Google Analytics, SharePoint/Excel or similar)

• Self-driven, pragmatic, well-documented working style; comfortable with ambiguity

   

Senior (4-5+ years of experience) — all of the above, plus:

• Proven experience designing Data Lake / Lakehouse architecture independently
• Ability to define and own a technical backlog and make architectural decisions with limited oversight
• Experience implementing data governance, lineage tracking, and security controls in regulated environments
• Comfortable working with GDPR, HIPAA, ePHI, and similar compliance frameworks

Nice to Have (both levels)

• Apache Iceberg or Delta Lake
• dbt
• Metabase or similar BI tooling

Recruitment Process

1. HR Screening call
2. Technical Interview with CTO
3. Final Interview with the client

If you want to develop professionally in a team of like-minded people and make this world better — we are waiting for your resume!

We are looking for a Mid/Senior Data Engineer to join our platform-focused team, working on the development and evolution of our CRM and data platform landscape.

The team operates as a platform and enablement unit, supporting different business areas and initiatives. A key focus area is the transition from a legacy CRM system to a new Salesforce-based solution, alongside ongoing work in building and extending data products and platform capabilities.

The setup currently consists of two Scrum teams, with Data Engineers shared across initiatives. The role is hands-on and involves working on data product creation, platform setup, and continuous improvements, with a growing focus on scaling and structuring the platform landscape

Required Skills:

· Strong experience in data engineering within enterprise environments

· Strong hands-on experience with Terraform, Docker, Python, SQL, AWS tools, Kubernetes, and Kafka

· Hands-on experience with modern data platform concepts such as Data Mesh (cloud object storage, open table formats, distributed processing)

· Solid Python skills for ETL development, data processing, and automation

· Experience designing, implementing, and maintaining reliable data pipelines

· Strong analytical, problem-solving, and system-level thinking skills

· Understanding of cloud technology lifecycle

· Fluent English in spoken and written communication

Preferred Experience (Nice-to-Have)

• Legacy data warehouse experience with Oracle or Sybase.

We are seeking a highly skilled AI/ML Engineer to design, build, and scale production-grade AI systems. The ideal candidate combines expertise in AI/ML, backend engineering, cloud infrastructure, and MLOps to deliver end-to-end AI solutions powered by LLMs, RAG architectures, and intelligent agents.

You will play a key role in developing scalable AI products, optimising inference performance, building AI platforms, and integrating cutting-edge AI capabilities into business applications.

Key Responsibilities

• Design and develop scalable AI/ML solutions for production environments.
• Build and deploy LLM-powered applications using OpenAI, Claude, Gemini, and Hugging Face ecosystems.
• Develop Retrieval-Augmented Generation (RAG) systems and agentic workflows.
• Create and maintain AI APIs and backend services using Python and FastAPI.
• Design data pipelines, vector search architectures, and AI orchestration workflows.
• Implement MLOps practices, including model deployment, monitoring, retraining, and observability.
• Optimise AI systems for latency, scalability, reliability, and cost efficiency.
• Collaborate with product, engineering, and data teams to deliver AI-driven features.
• Work with cloud-native infrastructure and containerised environments.
• Mentor junior engineers and contribute to architectural decisions.

Required Qualifications

• 5+ years of software engineering or machine learning experience.
• Strong proficiency in Python.
• Hands-on experience building and deploying production AI systems.
• Deep understanding of machine learning fundamentals, deep learning, NLP, and transformers.
• Experience with LLM technologies and frameworks:
  • OpenAI API
  • Claude API
  • Gemini
  • Hugging Face
  • LangChain
  • LangGraph
  • LlamaIndex
• Strong knowledge of:
  • RAG architectures
  • Vector databases
  • Embeddings
  • Agentic workflows
  • Prompt engineering
• Experience building REST APIs and backend services.
• Proficiency with Docker and cloud platforms (AWS, Azure, or GCP).
• Experience with SQL databases and data processing frameworks.

Preferred Qualifications

• Kubernetes and container orchestration experience.
• Infrastructure as Code using Terraform or similar tools.
• MLOps platforms such as MLflow, Kubeflow, or Airflow.
• Experience with Spark and large-scale data processing.
• Knowledge of Redis, Kafka, RabbitMQ, or real-time systems.
• Experience with AI observability, monitoring, and model lifecycle management.
• Exposure to enterprise AI integrations and AI governance.

Technical Stack

Languages

• Python
• SQL

Backend

• FastAPI
• Flask
• REST APIs
• GraphQL

AI/ML

• OpenAI
• Claude
• Gemini
• Hugging Face
• LangChain
• LangGraph
• LlamaIndex
• RAG
• AI Agents

Data

• PostgreSQL
• MySQL
• Pinecone
• Weaviate
• Qdrant
• ChromaDB

Cloud & DevOps

• AWS / Azure / GCP
• Docker
• Kubernetes
• Terraform
• GitHub Actions / GitLab CI