Jobs Data Engineer

We are seeking a proactive Senior Data Engineer to join our vibrant team. As a Senior Data Engineer, you will play a critical role in designing, developing, and maintaining sophisticated data pipelines, Ontology Objects, and Foundry Functions within Palantir Foundry. The ideal candidate will possess a robust background in cloud technologies, data architecture, and a passion for solving complex data challenges.
 

Key Responsibilities:

• Collaborate with cross-functional teams to understand data requirements, and design, implement and maintain scalable data pipelines in Palantir Foundry, ensuring end-to-end data integrity and optimizing workflows.
• Gather and translate data requirements into robust and efficient solutions, leveraging your expertise in cloud-based data engineering. Create data models, schemas, and flow diagrams to guide development.
• Develop, implement, optimize and maintain efficient and reliable data pipelines and ETL/ELT processes to collect, process, and integrate data to ensure timely and accurate data delivery to various business applications, while implementing data governance and security best practices to safeguard sensitive information.
• Monitor data pipeline performance, identify bottlenecks, and implement improvements to optimize data processing speed and reduce latency. 
• Troubleshoot and resolve issues related to data pipelines, ensuring continuous data availability and reliability to support data-driven decision-making processes.
• Stay current with emerging technologies and industry trends, incorporating innovative solutions into data engineering practices, and effectively document and communicate technical solutions and processes.
   

Tools and skills you will use in this role:

• Palantir Foundry
• Python
• PySpark
• SQL
• TypeScript
   

Required:

• 5+ years of experience in data engineering, preferably within the pharmaceutical or life sciences industry;
• Strong proficiency in Python and PySpark;
• Proficiency with big data technologies (e.g., Apache Hadoop, Spark, Kafka, BigQuery, etc.);
• Hands-on experience with cloud services (e.g., AWS Glue, Azure Data Factory, Google Cloud Dataflow);
• Expertise in data modeling, data warehousing, and ETL/ELT concepts;
• Hands-on experience with database systems (e.g., PostgreSQL, MySQL, NoSQL, etc.);
• Proficiency in containerization technologies (e.g., Docker, Kubernetes);
• Effective problem-solving and analytical skills, coupled with excellent communication and collaboration abilities;
• Strong communication and teamwork abilities;
• Understanding of data security and privacy best practices;
• Strong mathematical, statistical, and algorithmic skills.

Position Overview

We are looking for a Junior SRE / DevOps Engineer to join our infrastructure team and help support Kubernetes environments, CI/CD pipelines, application deployments, and production systems.

This role is a good fit for someone who already has basic hands-on experience with Linux, containers, Kubernetes, and CI/CD and wants to grow into an SRE / DevOps position.

Responsibilities

• Support Kubernetes-based environments.
• Assist with application deployments and CI/CD pipelines.
• Work with Jenkins pipelines.
• Support Java application build processes using Maven (mvn).
• Help troubleshoot deployment and infrastructure issues.
• Monitor applications and infrastructure.
• Work with logs, metrics, and alerts.
• Assist with infrastructure automation.
• Maintain technical documentation.
• Work with developers and senior DevOps/SRE engineers on production issues and improvements.

Required Skills

• Basic Linux administration skills.
• Basic understanding of Kubernetes.
• Understanding of Docker and containers.
• Basic experience with Jenkins or another CI/CD system.
• Basic understanding of Maven (mvn) and Java application build processes.
• Understanding of Git.
• Basic networking knowledge: DNS, TCP/IP, HTTP/HTTPS.
• Ability to read logs and troubleshoot basic application or infrastructure issues.
• Basic scripting skills with Bash or Python.
• Willingness to learn and work with production infrastructure.

Nice to Have

• Experience with Helm.
• Experience with Prometheus and Grafana.
• Basic knowledge of cloud platforms such as AWS, Azure, or GCP.
• Experience with Terraform or other Infrastructure-as-Code tools.
• Knowledge of Dremio.
• Basic understanding of Apache Arrow.
• Experience with PostgreSQL or object storage.
• Understanding of SRE concepts such as monitoring, alerting, availability, and incident response.

What We Expect

We do not expect a Junior engineer to know everything.

The important things are strong fundamentals, willingness to learn, and the ability to investigate problems instead of only following predefined instructions.

The engineer will work together with more experienced team members and gradually take more responsibility for CI/CD, Kubernetes, monitoring, and production infrastructure.

ZentixSoft is looking for a Data Architect! 🚀

Format: Full-Time Outstaffing, long-term contract (12 months), 100% Remote.

💡 Why ZentixSoft?
 💸 Salary Review: A structured system of compensation reviews aligned with your engineering growth. 
⚖️ Work-Life Balance: Sustainable workflows with no senseless overtime, helping you keep your resource fully charged. 
🦾 Trust and Transparency: Zero micromanagement and no invasive time-trackers—we fully trust your professional autonomy. 
🎁 Gifts & Culture: Corporate gifts for holidays, birthdays, and a true team atmosphere where your voice always matters.
 

🧩 Role Overview & Responsibilities:

We are looking for a hands-on Data Architect to take full architecture ownership of a complex data platform. In this role, you will assess, stabilize, improve, and modernize the existing architecture, delivering rapid, actionable engineering recommendations.

• Architecture Ownership: Define tactical (immediate stabilization) and target (long-term modernization) architectures that engineering teams can directly execute.
• Platform Modernization: Assess complex, legacy data environments; identify performance/scalability bottlenecks; and deliver clear, implementation-ready architectural outputs.
• Unstructured & Structured Data: Architect data pipelines and modeling frameworks capable of handling unstructured data (documents, PDFs, free text, scanned content, images) and structured/file-based datasets.
• Tech-Neutral Evaluation: Evaluate enterprise technologies based on scale, cost, workload, and business requirements rather than preference (determining when Databricks, Synapse, ADF, or Azure SQL are truly justified).
• Data Modeling & Pipelines: Design robust ETL/ELT frameworks, dimensional schemas, star schemas, and incremental processing/refresh strategies.
• Documentation & Governance: Create detailed architecture diagrams, technical decision logs, and data governance standards.
   

🎓 Our Perfect Match (Requirements):

• Proven Architecture Ownership: 4+ years of recent, demonstrated experience as a Data Architect owning overall Azure Data Platform architectures (candidate must be an Architect, not solely a Data Engineer).
• Azure Data Platform Expertise: Extensive hands-on background with Azure Data Services, including Azure SQL / SQL Server, Azure Data Lake, Azure Data Factory (ADF), Azure Synapse, and Databricks.
• Azure Cosmos DB (MUST-HAVE): Hands-on experience working with Azure Cosmos DB and file-based data stores within enterprise environments.
• Data Engineering Core: Strong mastery of Python, SQL, ETL/ELT, and Lakehouse architectures.
• Unstructured Data Experience: Proven track record handling unstructured data sources (documents, PDFs, text, scanned files).
• Fast Delivery & Problem Solving: Ability to onboard quickly into complex environments, navigate incomplete documentation, and deliver meaningful architectural guidance rapidly.
• Communication & English: Fluent English (C1) with exceptional technical writing and stakeholder communication skills.
   

📍 Project Details:

⏳ Duration: 12 Months (Full-time, 100% Remote).

🕒 Time Zone: CET / CEST.
 

We look forward to receiving your CV (please ensure your CV details your project contexts, architecture ownership, and hands-on Cosmos DB/Azure experience).

Description

Our client is looking for a hands-on Principal Data Engineer & Architect to lead the design, evolution, and execution of their core data engineering ecosystem. This role is built specifically for an elite technical leader who loves mapping out highly reliable distributed architectures but is driven by a strong desire to stay close to the metal—allocating approximately 70% of their time to writing mission-critical production code, core data libraries, and internal frameworks, and 30% to high-level architectural design, data infrastructure governance, and technical mentorship.
 

About Our Client

Our client is a venture-backed, AI-powered platform built specifically for consumer brands. It connects seamlessly to fragmented external sources like banks, POS systems, and advertising platforms to deliver daily profit & loss snapshots, cash flow plans, and peer benchmarking—all in real time. With integrations stretching across finance, e-commerce, and marketing tools, the platform provides the clarity and automation brands need to grow confidently.
 

What You’ll Do

Architect Data Infrastructure (70% Focus): Maintain a heavy, hands-on presence in the codebase. Author core data processing libraries, distributed computing frameworks, and high-scale data platforms in JavaScript (Node.js/NestJS) and Python to establish gold standards for the engineering team.

Design Data Systems (30% Focus): Design and evolve a robust, multi-tenant data architecture capable of handling massive datasets and high-throughput processing across hundreds of distinct data domains concurrently.

Data Quality & Governance: Establish bulletproof data integrity and observability patterns. Focus on robust processing workflows, comprehensive data validation, automated quality checks, and end-to-end distributed tracing across the data lifecycle.

Scale Data Pipelines: Define and enforce efficient data architectures, high-throughput streaming patterns, and transaction boundaries to guarantee accurate real-time financial reporting at scale.

Technical Leadership & Mentorship: Act as an elite technical sounding board for the engineering organization. Conduct comprehensive architecture reviews, upskill senior engineers, and champion modern DevOps, testing, and deployment workflows.
 

What We’re Looking For

Deep Industry Experience: 8+ years of professional software engineering experience, with a proven history of designing, deploying, and maintaining large-scale data processing and distributed data infrastructure solutions.

Polyglot Mastery in Data Engineering: Highly proficient in both JavaScript/TypeScript (Node.js, NestJS) and Python, with the ability to build sophisticated data pipelines and distributed systems using each language’s native strengths.

Advanced Data Engineering & Architecture Expertise: Extensively experienced with distributed systems, high-scale storage solutions, query optimization, and complex data architecture patterns for massive, high-velocity datasets.

Data Modeling & Schema Excellence: Expert-level understanding of relational (SQL) and non-relational (NoSQL) databases, schema design, complex data validation, mapping workflows, and ETL pipeline design.

True Builder Mentality: A strong architectural thinker who actively rejects the “ivory-tower” approach and remains deeply passionate about shipping high-quality production code every single week.
 

Would Be a Plus

Direct technical experience within FinTech, e-commerce SaaS platforms, or real-time data streaming architectures (e.g., Kafka, RabbitMQ).

Familiarity with financial data aggregates or banking networks (e.g., Plaid, Yodlee, Stripe).

Experience setting up data ingestion pipelines optimized for downstream AI/LLM analysis models.
 

What We Offer

Highly competitive principal-level base salary + substantial early-stage equity package.

Comprehensive medical, dental, and vision insurance coverage.

Flexible, remote-first working culture with an elite engineering group.

Direct technical influence over the foundational platform architecture of a fast-growing, venture-backed startup.

ORIL is looking for a Data Integration Specialist to lead and manage the data integration architecture of our AI-driven facilities management platform. The ideal candidate will possess strong expertise in optimizing data pipelines, resolving intricate integration issues, and ensuring uncompromising data quality.

About the project

A platform shaping the future of facilities management by connecting buildings, occupants, service providers, and operatives. It streamlines soft FM services so property managers and service providers can plan, source, and manage their portfolios more efficiently and at lower cost, delivering real-time transparency across every site. The ecosystem spans FM service workflows, IoT-connected building sensors, third-party FM platforms, mobile and web clients, and event-driven microservices.
 

What will you do?

• Design, build, and maintain scalable data pipelines and integrations across internal microservices, third-party FM platforms, IoT sensors, and external APIs.
• Design and implement ETL/ELT pipelines on Azure Databricks, building on Delta Lake and Lakehouse architecture to ingest, model, transform, and serve structured and semi-structured data.
• Define and own the data modelling layer – schemas and domain models that keep data structured and validated before ingestion, avoiding raw data sprawl.
• Work with event-driven architectures (message queues, pub/sub, event streams) to enable real-time data propagation and workflow automation.
• Define and enforce data mapping, transformation rules, and validation standards across all data flows.
• Collaborate with backend engineers, product managers, and data stakeholders to translate integration requirements into robust solutions.
• Monitor integration health, set up alerting, and diagnose pipeline failures to maintain high availability and data fidelity.
• Document integration patterns, data contracts, and API specifications for the wider engineering team.
• Contribute to platform-wide data quality, observability, and reliability standards.
   

What are we looking for?

• 4+ years of professional experience in a data integration, data engineering, or backend integration role.
• Hands-on experience with Azure Databricks for data engineering, pipeline orchestration, and large-scale transformations.
• Strong proficiency in Python for data engineering, pipeline development, and transformation logic.
• Strong understanding of data modelling, schema design, and data transformation patterns.
• Experience with relational and/or NoSQL databases (PostgreSQL, Cosmos DB, or similar).
• Understanding of distributed systems, microservices architectures, and cloud-native environments.
• High attention to detail and a strong sense of ownership over data quality and integration reliability.
• Comfort working across engineering, product, and business teams.
• English: Upper-Intermediate+

Nice to have:

• Experience with Azure Service Bus or similar event-driven messaging platforms (pub/sub, message queues, async workflows).
• Exposure to real-time streaming data and sensor telemetry workflows.
• Knowledge of data governance, lineage tracking, or observability tooling.
   

We offer:

• Competitive compensation;
• Accounting support services;
• Flexibility to work from anywhere (fully remotely or in our office in Lviv);
• Comprehensive day-off policy, including public holidays and special events;
• Education & certification budget + corporate education events;
• Wellness budget;
• An agile environment and culture that promotes personal and team development.

Job Description

Implement the metadata, catalog, semantic, discovery, and AI enablement capabilities that allow users and agents to find, understand, and consume enterprise data.

• Strong experience with SQL, Python, and cloud-based platforms like AWS, GCP, or Azure.
• Solid understanding of data modelling, pipelines and orchestration tools (Airflow, dbt, or similar).
• Familiarity with modern data ecosystems — e.g. Spark, Databricks, Snowflake, BigQuery or Redshift.
• Experience working with CI/CD, version control and testing best practices.
• A proactive approach to solving problems and improving systems.
• Excellent communication skills — you can translate technical concepts into language anyone can understand.
• A passion for data and how it can drive better decisions in media, marketing and beyond. 

Experience with technologies and platforms such as

• OpenMetadata, DataHub, Atlan
• Knowledge graph and semantic technologies
• Neo4j and graph databases
• Vector databases, RAG architectures, MCP frameworks
• Metadata management and data catalog platforms

Job Responsibilities

• Building metadata and catalog services
• Implementing knowledge graph and semantic capabilities
• Delivering data discovery and search experiences
• Enabling AI-ready data foundations
• Building context services for AI agents and applications
• Providing subject matter expertise on metadata and AI data services

Department/Project Description

Build the core data engineering capability that enables WPP Open applications, AI agents, analytics products, and business workflows to securely discover, access, combine, govern, and activate data across enterprise systems, client environments, media platforms, and third-party providers.

We are seeking senior engineers who have:

• Delivered enterprise-scale platform capabilities rather than traditional ETL solutions.
• Led implementation of complex data platforms in production environments.
• Worked with cloud-native technologies on Microsoft, Google Cloud, AWS, or comparable ecosystems.
• Built reusable services, APIs, integrations, and platform components.
• Supported operational applications and AI workloads, not just reporting and analytics.
• Operated within highly governed enterprise environments.
• Experience implementing data virtualization, federation, metadata platforms, governance capabilities, media data integration, or similar large-scale platform services.
• Strong opinions informed by hands-on delivery experience and a willingness to contribute to technical direction.

Job Description
Implement the platform capabilities that enable disparate data sources to be combined, virtualized, standardized, and exposed through a consistent access model without requiring extensive physical consolidation.

Experience with technologies and platforms such as

• Microsoft Fabric, OneLake, Synapse, Azure Data Lake, Purview
• BigQuery, BigLake, Dataplex
• Athena, Redshift Spectrum, Lake Formation, Glue
• Data virtualization, federated query, semantic layer, or data fabric platforms
• Strong Python

Job Responsibilities

• Implementing data virtualization and federation capabilities
• Building federated query services
• Developing semantic and abstraction layers
• Creating reusable APIs and access services
• Performance tuning, scalability, reliability, and operational support
• Contributing to platform design and technology decisions through hands-on expertise

Department/Project Description
Build the core data engineering capability that enables WPP Open applications, AI agents, analytics products, and business workflows to securely discover, access, combine, govern, and activate data across enterprise systems, client environments, media platforms, and third-party providers.

We are seeking four senior engineering specialists who will be responsible for implementation and delivery of key platform capabilities. While architecture leadership is provided by the platform architecture team, these hires are expected to bring deep expertise, contribute to solution design, challenge assumptions, and guide implementation decisions based on industry experience.

Join an exciting journey to create a cutting-edge Consumer Data Lake for a leading global organization based in Europe. This platform will unify, process, and leverage consumer data from various systems, unlocking advanced analytics, insights, and personalization opportunities. As a Senior Data Engineer, you will play a pivotal role in shaping and implementing the platform's architecture, focusing on hands-on technical execution and collaboration with cross-functional teams.

Your work will transform consumer data into actionable insights and personalization on a global scale. Using advanced tools to tackle complex challenges, you’ll innovate within a collaborative environment alongside skilled architects, engineers, and leaders.

Key Responsibilities:

• Hands-On Development: Build, maintain, and optimize data pipelines for ingestion, transformation, and activation.
• Create and implement scalable solutions to handle diverse data sources and high volumes of information
• Data Modeling & Warehousing: Design and maintain efficient data models and schemas for a cloud-based data platform.
• Develop pipelines to ensure data accuracy, integrity, and accessibility for downstream analytics.
• Collaboration: Partner with Solution Architects to translate high-level designs into detailed implementation plans.
• Work closely with Technical Product Owners to align data solutions with business needs.
• Collaborate with global teams to integrate data from diverse platforms, ensuring scalability, security, and accuracy.
• Platform Development: Enable data readiness for advanced analytics, reporting, and segmentation.
• Implement robust frameworks to monitor data quality, accuracy, and performance.
• Testing & Quality Assurance: Implement robust security measures to protect sensitive consumer data at every stage of the pipeline
• Ensure compliance with data privacy regulations (e.g., GDPR, CCPA ..) and internal policies.
• Monitor and address potential vulnerabilities, ensuring the platform adheres to security best practices.

Requirements:

• Over 4+ years of experience showcasing technical expertise and critical thinking in data engineering.
• Hands-on experience with DBT and strong Python programming skills.
• Proficiency in Snowflake and expertise in data modeling are essential.
• Demonstrated experience in building consumer data lakes and developing consumer analytics capabilities is required.
• In-depth understanding of privacy and security engineering within Snowflake, including concepts like RBAC, dynamic/tag-based data masking, row-level security/access policies, and secure views.
• Ability to design, implement, and promote advanced solution patterns and standards for solving complex challenges.
• Familiarity with multiple cloud platforms (Azure or GCP preferred, with a focus on Azure).
• Practical experience with Big Data batch and streaming tools.
• Competence in SQL, NoSQL, relational database design (SAP HANA experience is a bonus), and efficient methods for data retrieval and preparation at scale.
• Proven ability to collect and process raw data at scale, including scripting, web scraping, API integration, and SQL querying.
• Experience working in global environments and collaborating with virtual teams.
• A Bachelor’s or Master’s degree in Data Science, Computer Science, Economics, or a related discipline.

Description

The Client is the UK’s leading fee-free mortgage broker. They are seeking experienced Data Engineer to join client’s data team. This role focuses heavily on core data engineering, architecture, and pipeline delivery within established frameworks rather than the presentation layer. The ideal candidate will be responsible for designing, building and optimizing scalable ingestion-to-consumption patterns and modern data warehousing principles.

Requirements

Must-Have Skills:

• Python / PySpark for Data Engineering: Strong experience writing PySpark notebooks in Fabric, building data transformation/cleansing logic, and notebook-based pipeline development.
• Medallion Architecture: Proven expertise in designing and implementing Bronze/Silver/Gold layered ingestion-to-consumption patterns, incremental loading, and transformation logic.
• Dimensional Modelling: Strong knowledge of Kimball-style fact/dimension design, SCD (Slowly Changing Dimension) handling, and Gold-layer conformance.
• T-SQL & DDL Proficiency: Expertise in warehouse schema design, writing stored procedures, and performance tuning.
• Pipeline Development & Orchestration: Practical experience with Dataflows Gen2, Fabric pipelines, notebook orchestration, scheduling, and dependency management.
• Good written and verbal English communication skills.

Nice-to-Have Skills:

• Governance & Security Implementation: Exposure to Microsoft Purview integration, Entra ID mapping, and workspace/access control architecture.
• Power BI & Semantic Modelling: Basic knowledge of DAX, semantic model design, and row/column/object-level security (RLS/CLS/OLS).

Job responsibilities

• Data Pipeline & Framework Development: Design, build, and orchestrate robust end-to-end data pipelines using Dataflows Gen2, PySpark notebooks and native orchestration tools.
• Architecture & Storage Layer Management: Implement and maintain Medallion Architecture (Bronze, Silver, Gold layers), ensuring proper incremental loading strategies and layer-specific transformation logic.
• Data Warehousing & Dimensional Modelling: Build Kimball-style fact and dimension models, manage Slowly Changing Dimensions (SCDs), and ensure Gold-layer data conformance for analytics consumption.
• Data Transformation & Optimization: Write clean, modular Python/PySpark code and T-SQL/DDL logic for data cleansing, schema design, stored procedures, and query performance tuning.

Description

The Client is one of the major banks in the UK. This project is a strategic initiative to migrate legacy data infrastructure, including approximately 500 BTEQ scripts and 1,100 tables, to a modern cloud environment. GlobalLogic is establishing a dedicated Features Team to partner with the Client’s engineering leadership. Our team will focus on replatforming business logic, updating data pipelines and repointing SAS dependencies. Core technical requirements for the role include cloud stack knowledge, Python, SQL and DBT. We’re interested in problem solvers with the right mindset and can-do attitude to shape a strong team and build a a long-term collaborative partnership.

Requirements

To be successful in this role, you will need the following:

• Strong hands-on data engineering experience delivering production-grade solutions.
• Ability to design, implement and optimise complex data pipelines independently.
• Strong SQL and programming (Python preferred); capable of building reusable and scalable code.
• Experience with modern orchestration tools (e.g. Airflow, Step Functions, etc.).
• Hands-on experience on any cloud platform (AWS preferred or equivalent).
• Ability to proactively analyse existing processes, suggest improvements, and drive technical solutions end-to-end.
• Comfortable engaging with stakeholders, understanding business requirements, and translating them into technical design.
• Good written and verbal English communication skills.

Good to have:

• Experience with dbt Core for data transformations.
• Exposure to migrating legacy data platforms (Teradata, Hadoop, SAS) into modern cloud architectures.
• Familiarity with Databricks or similar platforms for advanced analytics.
• Understanding of data security, regulatory, and risk management principles in banking.

Job responsibilities

• Develop, maintain, and execute dbt and Python scripts to migrate database tables and views and integrate them with Databricks.
• Participate in data migration activities, ensuring reliable and efficient transformation of legacy data assets.
• Work closely with Barclays stakeholders to operate and support DIH and other internal data migration tools.
• Assist in the design and implementation of data transformation pipelines within the Databricks ecosystem.
• Validate migrated datasets and ensure data quality, consistency, and completeness throughout the migration process.
• Troubleshoot and resolve issues related to data transformation, pipelines, and integrations.
• Contribute to documentation related to data migration processes, dbt models, and pipeline architecture.
• Collaborate with cross-functional teams including data engineers, architects, and business stakeholders to deliver migration tasks.

Phoenix Games is a technology company building next-generation iGaming and Web3 products for a global audience. We leverage AI, cloud technologies, blockchain, and advanced analytics to create intelligent, data-driven gaming experiences.

As we continue to scale, we're looking for Data Engineer / Data Scientist to help us build modern data infrastructure, develop machine learning solutions, and turn massive volumes of data into actionable insights.

What You'll Do

• Design, build, and maintain scalable data pipelines and ETL/ELT processes.
• Develop and optimize data warehouses and data lake architectures.
• Collect, clean, and transform large datasets from multiple sources.
• Build predictive models, recommendation systems, and analytical solutions.
• Apply machine learning techniques to improve player engagement, fraud detection, personalization, and business intelligence.
• Design dashboards and reporting solutions for business stakeholders.
• Collaborate with Product Managers, Engineers, and BI teams to define data-driven strategies.
• Optimize data processing performance and ensure data quality.
• Implement monitoring, validation, and governance for data pipelines.
• Research and evaluate new AI and machine learning technologies.

Tech Stack

• Python
• SQL
• PostgreSQL
• Snowflake / BigQuery / Redshift
• Apache Airflow
• Spark
• Pandas
• NumPy
• Scikit-learn
• TensorFlow / PyTorch
• MLflow
• Docker
• Kubernetes
• AWS / Azure / GCP
• Git
• Power BI / Looker / Tableau

Requirements

• 2+ years of commercial experience in Data Engineering, Data Science, or Machine Learning.
• Strong Python programming skills.
• Advanced SQL knowledge.
• Experience building scalable data pipelines.
• Experience with cloud data platforms.
• Strong understanding of data modeling and database design.
• Experience with machine learning algorithms and model evaluation.
• Experience working with large datasets.
• Familiarity with Docker and cloud environments.
• Strong analytical and problem-solving skills.
• Upper-Intermediate English or higher.

Nice to Have

• Experience with LLMs, Generative AI, or AI agents.
• Knowledge of real-time data streaming (Kafka, RabbitMQ).
• Experience with feature engineering and MLOps.
• Experience with fraud detection or recommendation systems.
• Familiarity with blockchain analytics or Web3 ecosystems.

What We Offer

• Fully remote work from anywhere in Europe.
• Long-term B2B cooperation.
• Competitive salary paid in EUR or USD.
• Annual salary reviews.
• Performance-based bonus program.
• Annual company bonus.
• 25 paid vacation days.
• Paid sick leave.
• Flexible working hours.
• Home office budget.
• Laptop or equipment allowance.
• Learning & development budget for courses, certifications, conferences, and books.
• English language lessons.
• Private health insurance.
• Mental health support program.
• Sports and wellness compensation.
• Team-building activities and international company retreats.
• Opportunity to work on AI, Web3, blockchain, and large-scale data platforms.
• Clear career growth opportunities within a rapidly expanding international product company.

Why Join Phoenix Games?

At Phoenix Games, data is at the core of every product decision. You'll work with modern technologies, solve complex data challenges, and help build intelligent systems that power the next generation of online gaming.

If you're passionate about data engineering, machine learning, and creating impactful AI-driven solutions, we'd love to hear from you.

TJHelpers is committed to developing strong engineering teams through mentorship, real project experience, and our “Helpers as a Service” model. We provide structured growth, hands-on practice, and a supportive environment where engineers can evolve quickly and meaningfully.

We are looking for a Junior Data Engineer to join our team and help build and maintain reliable data pipelines, transformations, and datasets for modern products in fintech, AI, telecommunications, healthcare, and other data-driven domains.

This is a great opportunity to work with real-world data, grow in data engineering, and gain hands-on experience with SQL, Python, ETL/ELT processes, and modern data platforms.

Responsibilities

• Support the development and maintenance of data pipelines and ETL/ELT processes 
• Collect, clean, transform, and load data from databases, APIs, files, and external systems 
• Write and optimize SQL queries for data preparation and transformation 
• Assist in building datasets for analytics, reporting, and internal products 
• Support data quality, consistency, and reliability across pipelines and datasets 
• Work with structured and semi-structured data 
• Participate in troubleshooting and improving data processes 
• Assist in monitoring and improving data pipeline reliability and performance 
• Collaborate with engineers, analysts, and product teams 

Requirements

• Basic understanding of data engineering concepts and data pipeline architecture 
• Hands-on experience writing SQL queries and working with Python 
• Understanding of relational databases such as PostgreSQL or MySQL 
• Ability to write SQL queries using joins, filtering, aggregations, and transformations 
• Basic understanding of ETL/ELT processes 
• Understanding of data modeling and data transformation principles 
• Familiarity with data formats such as CSV and JSON and working with APIs 
• Basic understanding of structured and semi-structured data 
• Analytical thinking and attention to detail 

• Motivation to learn and grow in a technical environment 

   

Nice to Have

• Basic experience with Pandas 
• Basic familiarity with Spark or PySpark 
• Familiarity with Airflow, dbt, or similar tools 
• Understanding of data warehouses such as BigQuery, Snowflake, or Redshift 
• Familiarity with cloud platforms such as AWS, GCP, or Azure 
• Familiarity with Git 
• Experience with BI or analytics tools 

• Interest in data platforms, analytics engineering, or ML/AI workflows 

   

What We Offer

• Work with real production data systems and modern technologies 
• Mentorship from experienced engineers and tech leads 
• Clear growth path to Middle Data Engineer level 
• Supportive engineering culture focused on learning and practical development 
• Flexible schedule and fully remote work opportunities

TJHelpers is committed to developing strong engineering teams through mentorship, real project experience, and our “Helpers as a Service” model. We provide structured growth, hands-on practice, and a supportive environment where engineers can evolve quickly and meaningfully.

We are looking for a Senior AI/LLM Data Engineer (Technical Consultant) to join our team and help design, build, and scale modern data pipelines and retrieval systems for AI-driven products in fintech, healthcare, telecommunications, automation, and other data-intensive domains.

This role is ideal for an engineer who combines strong data engineering fundamentals with hands-on experience in LLM and RAG systems and can contribute not only through implementation, but also through architecture design, technical planning, and consulting-driven engineering decisions.

Responsibilities

• Design and develop scalable data pipelines for AI/LLM products and internal data platforms 
• Build and maintain ingestion, transformation, chunking, enrichment, indexing, and retrieval workflows 
• Prepare and structure data for RAG systems, semantic search, embeddings, and LLM evaluation workflows 
• Support fine-tuning and data preparation workflows for LLM use cases 
• Work with structured, semi-structured, and unstructured data from documents, APIs, databases, and external systems 
• Design and optimize retrieval pipelines, including metadata strategies, filtering, ranking, and relevance improvements 
• Integrate LLM-based solutions with data platforms, vector storage systems, search platforms, and backend services 
• Participate in architecture design, technical planning, and engineering decision-making for AI/LLM initiatives 
• Improve system quality, scalability, reliability, observability, and cost efficiency 
• Diagnose and resolve complex issues in production AI/data environments 
• Collaborate closely with engineers, product teams, analytics, and AI teams 
• Transform business requirements into practical AI/data engineering solutions 

Requirements

• Strong commercial experience in data engineering with hands-on work building data pipelines, ETL/ELT workflows, and distributed data processing systems 
• Hands-on experience with AI/LLM systems, RAG pipelines, embeddings, semantic search, or vector search/retrieval systems 
• Strong Python and SQL skills 
• Experience working with structured, semi-structured, and unstructured data in production environments 
• Strong understanding of data modeling, data transformation, validation, and data quality practices 
• Experience designing scalable and maintainable data workflows 
• Experience with modern data platforms and tools such as Airflow, dbt, Spark, Kafka, or similar technologies 
• Experience with vector databases or search platforms such as Pinecone, Weaviate, Milvus, Elasticsearch, or OpenSearch 
• Understanding of data preparation and optimization techniques for LLM use cases, including chunking, metadata strategies, and retrieval quality improvements 
• Experience with cloud platforms such as AWS, GCP, or Azure 
• Experience with Docker, CI/CD pipelines, and production deployment practices 
• Ability to work independently, make technical decisions, and clearly communicate trade-offs and architectural choices 
• Experience participating in technical consulting, solution design, or client-facing technical discussions 
• English level: B2 or higher 

Nice to Have

• Experience with LLM evaluation frameworks, prompt pipelines, or fine-tuning workflows 
• Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks 
• Experience with reranking, hybrid search, recommendation systems, or knowledge graphs 
• Understanding of MLOps, LLMOps, observability, and experimentation workflows 
• Experience with modern data warehouse or lakehouse platforms such as BigQuery, Snowflake, Redshift, or Databricks 
• Understanding of security, governance, and privacy practices in AI/data systems 

What We Offer

• Work on real-world AI/LLM and data-intensive products 
• Opportunity to participate in architecture, technical discovery, and solution design — not only implementation 
• Collaboration with strong engineers, architects, and product teams 
• Clear growth path toward AI Architect, Data Platform Architect, or Technical Lead roles 
• Open engineering culture focused on ownership, transparency, and continuous improvement 
• Long-term projects, flexible schedule, and fully remote work opportunities

US Company is searching for a Data & Automation Engineer to join an AI-first product company building an advanced communications platform powered by Large Language Models (LLMs). This is an exciting opportunity to work across the full technology stack, designing scalable backend services, modern user interfaces, cloud infrastructure, and AI-powered features in close collaboration with a US-based engineering team. Full-time, official contract, remote work, and flexible schedule.
 

Brief project description:
The product is an AI-driven communications platform that enables businesses to automate and enhance customer interactions through voice, messaging, and intelligent workflows powered by modern LLM technologies.

Responsibilities:

• Build and maintain data workflows using PostgreSQL and AWS.
• Automate processes with Lambda, EventBridge, and APIs.
• Develop and maintain reports, dashboards, and data integrations.
• Troubleshoot data issues and ensure data quality and reliability..

Required skills (must have):

• Strong PostgreSQL/SQL skills: 5+ years.
• Hands-on AWS experience, especially Lambda and EventBridge.
• Experience with REST APIs and reporting/BI tools.
• Strong analytical, troubleshooting, and communication skills.
• 3+ years in data engineering, analytics, or a related technical role.
• Excellent English and ability to work independently.

Would be considered as a plus:

• Python or Node.js/TypeScript.
• Experience with additional AWS services.
• Experience with BI tools such as Power BI, Tableau, or Metabase.
• Experience with ETL/ELT, dbt, or Infrastructure-as-Code.

Work conditions:

• 100% remote work.
• Full-time (40 hours per week).
• Official contract.
• Flexible schedule with required overlap with US business hours.

Interviewing / Hiring process:
Step 1 - preliminary interview (main questions) - 45 mins
Step 2 - internal technical interview - 60 mins
Step 3 - technical interview with Team Leader - 60 mins

We are seeking a skilled ML/AI Engineer to join our team and deliver cutting-edge ML/AI models in the ASR domain and legal insights more broadly. The core of this role is building, deploying, and serving ML models in production. 

A key part of this role is speech engineering — prior ASR/speech experience is not a must, but a willingness to learn and grow into this domain is essential. Experience with LLM-based solutions and agentic flows is an advantage, not a requirement. 

What you’ll do: 

• Build, deploy, and serve ML models in production — the core of the role — optimizing inference performance, latency, and efficient GPU utilization. 
• Support and extend our speech solutions, including 3rd-party ASR integrations and self-hosted ML models. 
• Evaluate and optimize key ASR/ML metrics such as WER, latency, F1 score, EER, and DER. 
• Secondary: Integrate LLMs and agentic flows into production solutions where they add value, including fine-tuning existing models. 

What you’ll bring: 

Core (required): 

• Solid background in machine/deep learning, with 4+ years of experience. 
• Strong Python skills and experience working in Linux environments. 
• Familiarity with deep learning libraries (e.g., PyTorch, TensorFlow) and training workflows. 
• Experience with MLOps pipelines and serving/deploying models in production. 
• Experience optimizing models for inference, including GPU acceleration. 
• Experience with Docker, gRPC, and serverless / micro-services architectures on cloud infrastructure (AWS/GCP/Azure). 
• Experience with real-time / streaming systems and low-latency processing (e.g. streaming pipelines, live sessions). 

Nice to have (secondary / optional): 

• Experience building and productizing agentic flows and AI-driven solutions (1+ year). 
• Familiarity with LLM / agent orchestration frameworks such as CrewAI, AWS Bedrock, Temporal, AgentCore, or LiteLLM. 
• Prior ASR / speech experience — not required; willingness to learn this domain is what matters most.