Jobs Data Engineer

• Join the Data Engineering team to contribute to the ongoing maintenance and improvement of an internal LLM-powered assistant that uses hosted LLM APIs and internal knowledge sources, with a focus on reliability, retrieval quality, and operational excellence.

• Responsibilities:

  • Maintain and enhance ingestion/enrichment pipelines for internal content (parsing/extraction, normalization, metadata enrichment, deduplication, and quality monitoring)
  • Improve indexing and retrieval performance and quality (chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching) and support hybrid retrieval capabilities (vector + keyword/BM25 + metadata)
  • Implement and maintain access-aware retrieval by propagating/enforcing document permissions through indexing and query-time filters, including audit logs and validation tests
  • Improve source attribution so responses reliably point to the correct documents and sections in a consistent format.
  • Extend and harden tool/workflow execution and automations (scheduled/trigger-based), including retries, timeouts, idempotency, concurrency controls, and run history
  • Develop and maintain evaluation and regression testing (golden sets, automated scoring) and support structured comparisons across LLM providers/models as required
  • Operate the platform in production: observability (logs/metrics/tracing), alerting, incident support, performance tuning, and cost controls, plus runbooks and handover documentation

• Mandatory Skills Description:

  8+ years of hands-on experience in Data Science and 5+ years in Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.
  Strong programming skills in languages such as Python and familiarity building ETL pipelines.
  Expertise in SQL and experience with both relational (preferably Postgres) and NoSQL databases (Open Search or Elastic Search)
  Familiarity with cloud platforms (e.g., AWS) and their services.
  Experience with version control systems (e.g., Git) and CI/CD pipelines.
  Ability to build scalable infrastructure to embed and search very large number of documents.
  Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.
  Expertise in ML inference optimizations
  Solid experience with Hybrid RAG, chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching, etc.
  Knowledge of network optimization for distributed ML training and inference.
  Understanding of distributed training patterns and checkpointing strategies.
  Strong English skills (B2 and higher)
  Strong verbal and written communication skills.
  Ability to work independently and collaborate in a group.

• Nice-to-Have Skills Description:

  Agile certification
  AWS/Oracle/Microsoft attestations and certifications
  Domain knowledge - Trading and Capital Markets

• Languages:
  • English: C1 Advanced

We are seeking a highly skilled Snowflake Data Engineer with 7 years of IT experience to design, build, and optimize scalable data pipelines and cloud-based solutions across AWS, Azure, and GCP. The ideal candidate will have strong expertise in Snowflake, ETL Tools like DBT, Python, visualization tools like Tableau and modern CI/CD practices, with a deep understanding of data governance, security, and role-based access control (RBAC). Knowledge of data modeling methodologies (OLTP, OLAP, Data Vault 2.0), data quality frameworks, Stream lit application development and SAP integration and infrastructure-as-code with Terraform is essential. Experience working with different file formats such as JSON, Parquet, CSV, and XML is highly valued.

• Responsibilities:

  • Design and develop data pipelines using Snowflake and Snow pipe for real-time and batch ingestion.
  • Implement CI/CD pipelines in Azure DevOps for seamless deployment of data solutions.
  • Automate DBT jobs to streamline transformations and ensure reliable data workflows.
  • Apply data modeling techniques including OLTP, OLAP, and Data Vault 2.0 methodologies to design scalable architectures.
  • Document data models, processes, and workflows clearly for future reference and knowledge sharing.
  • Build data tests, unit tests, and mock data frameworks to validate and maintain reliability of data solutions.
  • Develop Streamlit applications integrated with Snowflake to deliver interactive dashboards and self-service analytics.
  • Integrate SAP data sources into Snowflake pipelines for enterprise reporting and analytics.
  • Leverage SQL expertise for complex queries, transformations, and performance optimization.
  • Integrate cloud services across AWS, Azure, and GCP to support multi-cloud data strategies.
  • Develop Python scripts for ETL/ELT processes, automation, and data quality checks.
  • Implement infrastructure-as-code solutions using Terraform for scalable and automated cloud deployments.
  • Manage RBAC and enforce data governance policies to ensure compliance and secure data access.
  • Collaborate with cross-functional teams including business analysts, and business stakeholders to deliver reliable data solutions.

• Mandatory Skills Description:

  • Strong proficiency in Snowflake (Snowpipe, RBAC, performance tuning).
  • Hands-on experience with Python , SQL , Jinja , JavaScript for data engineering tasks.
  • CI/CD expertise using Azure DevOps (build, release, version control).
  • Experience automating DBT jobs for data transformations.
  • Experience building Streamlit applications with Snowflake integration.
  • Cloud services knowledge across AWS (S3, Lambda, Glue), Azure (Data Factory, Synapse), and GCP (BigQuery, Pub/Sub).

Join the Data Engineering team to contribute to the ongoing maintenance and improvement of an internal LLM-powered assistant that uses hosted LLM APIs and internal knowledge sources, with a focus on reliability, retrieval quality, and operational excellence.

• Responsibilities:

  • Maintain and enhance ingestion/enrichment pipelines for internal content (parsing/extraction, normalization, metadata enrichment, deduplication, and quality monitoring)
  • Improve indexing and retrieval performance and quality (chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching) and support hybrid retrieval capabilities (vector + keyword/BM25 + metadata)
  • Implement and maintain access-aware retrieval by propagating/enforcing document permissions through indexing and query-time filters, including audit logs and validation tests
  • Improve source attribution so responses reliably point to the correct documents and sections in a consistent format.
  • Extend and harden tool/workflow execution and automations (scheduled/trigger-based), including retries, timeouts, idempotency, concurrency controls, and run history
  • Develop and maintain evaluation and regression testing (golden sets, automated scoring) and support structured comparisons across LLM providers/models as required
  • Operate the platform in production: observability (logs/metrics/tracing), alerting, incident support, performance tuning, and cost controls, plus runbooks and handover documentation

• Mandatory Skills Description:

  8+ years of hands-on experience in Data Science and 5+ years in Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.
  Strong programming skills in languages such as Python and familiarity building ETL pipelines.
  Expertise in SQL and experience with both relational (preferably Postgres) and NoSQL databases (Open Search or Elastic Search)
  Familiarity with cloud platforms (e.g., AWS) and their services.
  Experience with version control systems (e.g., Git) and CI/CD pipelines.
  Ability to build scalable infrastructure to embed and search very large number of documents.
  Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.
  Expertise in ML inference optimizations
  Solid experience with Hybrid RAG, chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching, etc.
  Knowledge of network optimization for distributed ML training and inference.
  Understanding of distributed training patterns and checkpointing strategies.
  Strong English skills (B2 and higher)
  Strong verbal and written communication skills.
  Ability to work independently and collaborate in a group.

• Project Description:

  We are seeking a highly skilled Snowflake Data Engineer with 7 years of IT experience to design, build, and optimize scalable data pipelines and cloud-based solutions across AWS, Azure, and GCP. The ideal candidate will have strong expertise in Snowflake, ETL Tools like DBT, Python, visualization tools like Tableau and modern CI/CD practices, with a deep understanding of data governance, security, and role-based access control (RBAC). Knowledge of data modeling methodologies (OLTP, OLAP, Data Vault 2.0), data quality frameworks, Stream lit application development and SAP integration and infrastructure-as-code with Terraform is essential. Experience working with different file formats such as JSON, Parquet, CSV, and XML is highly valued.

• Responsibilities:

  • Design and develop data pipelines using Snowflake and Snow pipe for real-time and batch ingestion.
  • Implement CI/CD pipelines in Azure DevOps for seamless deployment of data solutions.
  • Automate DBT jobs to streamline transformations and ensure reliable data workflows.
  • Apply data modeling techniques including OLTP, OLAP, and Data Vault 2.0 methodologies to design scalable architectures.
  • Document data models, processes, and workflows clearly for future reference and knowledge sharing.
  • Build data tests, unit tests, and mock data frameworks to validate and maintain reliability of data solutions.
  • Develop Streamlit applications integrated with Snowflake to deliver interactive dashboards and self-service analytics.
  • Integrate SAP data sources into Snowflake pipelines for enterprise reporting and analytics.
  • Leverage SQL expertise for complex queries, transformations, and performance optimization.
  • Integrate cloud services across AWS, Azure, and GCP to support multi-cloud data strategies.
  • Develop Python scripts for ETL/ELT processes, automation, and data quality checks.
  • Implement infrastructure-as-code solutions using Terraform for scalable and automated cloud deployments.
  • Manage RBAC and enforce data governance policies to ensure compliance and secure data access.
  • Collaborate with cross-functional teams including business analysts, and business stakeholders to deliver reliable data solutions.

• Mandatory Skills Description:

  • Strong proficiency in Snowflake (Snowpipe, RBAC, performance tuning).
  • Hands-on experience with Python , SQL , Jinja , JavaScript for data engineering tasks.
  • CI/CD expertise using Azure DevOps (build, release, version control).
  • Experience automating DBT jobs for data transformations.
  • Experience building Streamlit applications with Snowflake integration.
  • Cloud services knowledge across AWS (S3, Lambda, Glue), Azure (Data Factory, Synapse), and GCP (BigQuery, Pub/Sub).

• Nice-to-Have Skills Description:

  - Cloud certifications is a plus

• Languages:
  • English: B2 Upper Intermediate

Project Description:

We are seeking a highly skilled Snowflake Data Engineer with 7 years of IT experience to design, build, and optimize scalable data pipelines and cloud-based solutions across AWS, Azure, and GCP. The ideal candidate will have strong expertise in Snowflake, ETL Tools like DBT, Python, visualization tools like Tableau and modern CI/CD practices, with a deep understanding of data governance, security, and role-based access control (RBAC). Knowledge of data modeling methodologies (OLTP, OLAP, Data Vault 2.0), data quality frameworks, Stream lit application development and SAP integration and infrastructure-as-code with Terraform is essential. Experience working with different file formats such as JSON, Parquet, CSV, and XML is highly valued.

Responsibilities:

• Design and develop data pipelines using Snowflake and Snow pipe for real-time and batch ingestion.
• Implement CI/CD pipelines in Azure DevOps for seamless deployment of data solutions.
• Automate DBT jobs to streamline transformations and ensure reliable data workflows.
• Apply data modeling techniques including OLTP, OLAP, and Data Vault 2.0 methodologies to design scalable architectures.
• Document data models, processes, and workflows clearly for future reference and knowledge sharing.
• Build data tests, unit tests, and mock data frameworks to validate and maintain reliability of data solutions.
• Develop Streamlit applications integrated with Snowflake to deliver interactive dashboards and self-service analytics.
• Integrate SAP data sources into Snowflake pipelines for enterprise reporting and analytics.
• Leverage SQL expertise for complex queries, transformations, and performance optimization.
• Integrate cloud services across AWS, Azure, and GCP to support multi-cloud data strategies.
• Develop Python scripts for ETL/ELT processes, automation, and data quality checks.
• Implement infrastructure-as-code solutions using Terraform for scalable and automated cloud deployments.
• Manage RBAC and enforce data governance policies to ensure compliance and secure data access.
• Collaborate with cross-functional teams including business analysts, and business stakeholders to deliver reliable data solutions.

Mandatory Skills Description:

• Strong proficiency in Snowflake (Snowpipe, RBAC, performance tuning).
• Hands-on experience with Python , SQL , Jinja , JavaScript for data engineering tasks.
• CI/CD expertise using Azure DevOps (build, release, version control).
• Experience automating DBT jobs for data transformations.
• Experience building Streamlit applications with Snowflake integration.
• Cloud services knowledge across AWS (S3, Lambda, Glue), Azure (Data Factory, Synapse), and GCP (BigQuery, Pub/Sub).

Nice-to-Have Skills Description:

- Cloud certifications is a plus

Languages:

English: B2 Upper Intermediate

We are looking for a Lead Data Engineer in Warsaw.
Location: Poland-based, with hybrid work from the Warsaw office 2–3 days per week. Fully remote is not available.

This role is for someone who wants to do more than just keep pipelines running. It’s a chance to build the data backbone for People Analytics and AI in a global enterprise setting.

You’d be working on the lakehouse foundation that brings together data from systems like SAP, ServiceNow, and more, with a strong focus on governance, scalability, and serving real business and AI use cases.

It’s a great fit for someone who enjoys ownership, likes building things properly, and wants their work to directly enable analytics, internal products, and AI solutions.
If you know someone strong in data engineering who may be a fit, I’d appreciate a share.

About the job

The Lead Data Engineer is responsible for building, maintaining, and continuously improving the central HR data lake that aggregates data from all systems. In this role, you will ensure the availability of reliable, analytics-ready datasets for internal tools, AI-driven applications, and cross-functional teams. You will act as a key partner to analysts, IT, and business stakeholders, enabling trusted insights, data-driven decisions, and smooth operation of the enterprise HR data ecosystem.

More About The Role

• Build and operate a Databricks Lakehouse / Data Lake for enterprise HR data.
• Integrate multiple data sources (e.g., SAP SuccessFactors, ServiceNow) to ensure seamless and accurate data flow.
• Implement and maintain analytics-ready data models in dbt and convert exploratory datasets into reusable, trusted derived datasets.
• Develop, refactor, and optimize data pipelines using AI-assisted coding tools (CLI-first) in Databricks for speed, quality, and maintainability.
• Provide curated datasets for internal tools, AI-driven applications, and reporting purposes.
• Partner with analysts and business stakeholders to understand data requirements and translate them into robust technical solutions.
• Monitor and improve data quality, governance, and performance across all pipelines and datasets.

More About You

• Minimum 5 years of experience in data engineering.
• Strong hands-on experience with Databricks, SQL, and Python.
• Extensive experience with analytics engineering tools such as dbt.
• Skilled in designing modular data systems (Design Thinking approach).
• Experience enforcing data governance and access management practices.
• Curious, pragmatic, and solution-oriented mindset.
• Careful and disciplined in handling sensitive data.
• Strong awareness of privacy, security, and compliance requirements.
• Fluent in English

More About What We Offer As one of the world’s leading hearing care providers headquartered in Switzerland, we are committed to building an inclusive culture. We create an environment where you can balance a successful career with your commitments and interests outside of work through our flexible hybrid working model. We offer a wide range of training opportunities for both your professional and personal development, along with exceptional growth opportunities supported by individual development plans.

Magdalena Suszek, Talent Acquisition Partner, is looking forward to receiving your application via our online job application platform. For this vacancy only direct applications will be considered.

Sonova is an equal opportunity employer. We team up. We grow talent. We collaborate with people of diverse backgrounds to win with the best team in the market place. We guarantee every person equal treatment in regard to employment and opportunity for employment, regardless of a candidate’s ethnic or national origin, religion, sexual orientation or marital status, gender, genetic identity, age, disability or any other legally protected status.

Are you passionate about building large-scale cloud data infrastructure that makes a real difference? We are looking for a Senior Data Engineer to join our team and work on an impactful healthcare technology project. This role offers a remote work format with the flexibility to collaborate across international teams.

At Sigma Software, we deliver innovative IT solutions to global clients in multiple industries, and we take pride in projects that improve lives. Joining us means working with cutting-edge technologies, contributing to meaningful initiatives, and growing in a supportive environment.

CUSTOMER
Our client is a leading medical technology company. Its portfolio of products, services, and solutions is at the center of clinical decision-making and treatment pathways. Patient-centered innovation has always been, and will always be, at the core of the company. The client is committed to improving patient outcomes and experiences, regardless of where patients live or what they face. The Customer is innovating sustainably to provide healthcare for everyone, everywhere. 

PROJECT
The project focuses on building and maintaining large-scale cloud-based data infrastructure for healthcare applications. It involves designing efficient data pipelines, creating self-service tools, and implementing microservices to simplify complex processes. The work will directly impact how healthcare providers access, process, and analyze critical medical data, ultimately improving patient care.

Responsibilities:

• Collaborate with the Product Owner and team leads to define and design efficient pipelines and data schemas
• Build and maintain infrastructure using Terraform for cloud platforms
• Design and implement large-scale cloud data infrastructure, self-service tooling, and microservices
• Work with large datasets to optimize performance and ensure seamless data integration
• Develop and maintain squad-specific data architectures and pipelines following ETL and Data Lake principles
• Discover, analyze, and organize disparate data sources into clean, understandable schemas

Requirements:

• Hands-on experience with cloud computing services in data and analytics
• Experience with data modeling, reporting tools, data governance, and data warehousing
• Proficiency in Python and PySpark for distributed data processing
• Experience with Azure, Snowflake, and Databricks
• Experience with Docker and Kubernetes
• Knowledge of infrastructure as code (Terraform)
• Advanced SQL skills and familiarity with big data databases such as Snowflake, Redshift, etc.
• Experience with stream processing technologies such as Kafka, Spark Structured Streaming
• At least an Upper-Intermediate level of English 

Are you passionate about building large-scale cloud data infrastructure that makes a real difference? We are looking for a Senior Data Engineer to join our team and work on an impactful healthcare technology project. This role offers a remote work format with the flexibility to collaborate across international teams.

At Sigma Software, we deliver innovative IT solutions to global clients in multiple industries, and we take pride in projects that improve lives. Joining us means working with cutting-edge technologies, contributing to meaningful initiatives, and growing in a supportive environment.

CUSTOMER
Our client is a leading medical technology company. Its portfolio of products, services, and solutions is at the center of clinical decision-making and treatment pathways. Patient-centered innovation has always been, and will always be, at the core of the company. The client is committed to improving patient outcomes and experiences, regardless of where patients live or what they face. The Customer is innovating sustainably to provide healthcare for everyone, everywhere. 

PROJECT
The project focuses on building and maintaining large-scale cloud-based data infrastructure for healthcare applications. It involves designing efficient data pipelines, creating self-service tools, and implementing microservices to simplify complex processes. The work will directly impact how healthcare providers access, process, and analyze critical medical data, ultimately improving patient care.

Responsibilities:

• Collaborate with the Product Owner and team leads to define and design efficient pipelines and data schemas
• Build and maintain infrastructure using Terraform for cloud platforms
• Design and implement large-scale cloud data infrastructure, self-service tooling, and microservices
• Work with large datasets to optimize performance and ensure seamless data integration
• Develop and maintain squad-specific data architectures and pipelines following ETL and Data Lake principles
• Discover, analyze, and organize disparate data sources into clean, understandable schemas

Requirements:

• Possibility to meet clients annually in Europe
• Hands-on experience with cloud computing services in data and analytics
• Experience with data modeling, reporting tools, data governance, and data warehousing
• Proficiency in Python and PySpark for distributed data processing
• Experience with Azure, Snowflake, and Databricks
• Experience with Docker and Kubernetes
• Knowledge of infrastructure as code (Terraform)
• Advanced SQL skills and familiarity with big data databases such as Snowflake, Redshift, etc.
• Experience with stream processing technologies such as Kafka, Spark Structured Streaming
• At least an Upper-Intermediate level of English 

Чи захоплюєтесь ви створенням масштабної хмарної інфраструктури даних, яка має реальний вплив? Ми шукаємо Senior Data Engineer, який приєднається до нашої команди та працюватиме над важливим проєктом у сфері медичних технологій. Ця роль передбачає віддалений формат роботи з можливістю співпрацювати з міжнародними командами.

У Sigma Software ми створюємо інноваційні ІТ-рішення для глобальних клієнтів у різних галузях і пишаємося проєктами, які покращують життя людей. Приєднавшись до нас, ви працюватимете з передовими технологіями, робитимете внесок у значущі ініціативи та розвиватиметеся в підтримуючому середовищі.

Клієнт:
Наш клієнт — провідна компанія у сфері медичних технологій. Її портфель продуктів, сервісів та рішень знаходиться в центрі процесів клінічного прийняття рішень і побудови шляхів лікування. Інновації, орієнтовані на пацієнтів, завжди були і залишаються основою діяльності компанії. Клієнт прагне покращувати результати лікування та досвід пацієнтів незалежно від того, де вони живуть і з якими викликами стикаються. Компанія інноваційно розвивається, щоб забезпечити доступ до медицини для кожного, всюди.

Проєкт:
Проєкт зосереджений на створенні та підтримці масштабної хмарної інфраструктури даних для медичних застосунків. Він включає проєктування ефективних data pipelines, створення self-service інструментів та реалізацію мікросервісів для спрощення складних процесів. Робота безпосередньо впливатиме на те, як медичні установи отримують доступ до критично важливих медичних даних, обробляють їх та аналізують, що зрештою покращує якість медичної допомоги пацієнтам.

 

Обов'язки: 

• Співпрацювати з Product Owner та технічними лідами для визначення та проєктування ефективних pipelines і схем даних
• Будувати та підтримувати інфраструктуру для хмарних платформ за допомогою Terraform
• Проєктувати та впроваджувати масштабну хмарну інфраструктуру даних, self-service інструменти та мікросервіси
• Працювати з великими наборами даних для оптимізації продуктивності та забезпечення безперебійної інтеграції даних
• Розробляти та підтримувати архітектури даних і pipelines для команди відповідно до принципів ETL та Data Lake
• Виявляти, аналізувати та впорядковувати різнорідні джерела даних у зрозумілі та чисті схеми

Вимоги:

• Можливість раз на рік зустрічатися з клієнтами в Європі
• Практичний досвід роботи з хмарними сервісами для даних та аналітики
• Досвід data modeling, reporting tools, data governance та data warehousing
• Впевнене володіння Python і PySpark для розподіленої обробки даних
• Досвід роботи з Azure, Snowflake та Databricks
• Досвід роботи з Docker та Kubernetes
• Знання infrastructure as code (Terraform)
• Просунуті навички SQL та знайомство з big data базами даних, такими як Snowflake, Redshift тощо
• Досвід роботи зі stream processing технологіями, такими як Kafka, Spark Structured Streaming
• Рівень англійської не нижче Upper-Intermediate

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

PwC Poland’s Cloud & Digital team supports global cloud projects across Cloud Strategy, Migrations, Data Analytics, and DevOps. We help clients adopt cloud infrastructure, modernize data platforms, and optimize operations. This hybrid role is based in Warsaw with flexible working options.

Your future role:

• Building data platforms (data warehouses, lakes, lakehouses) using Azure Databricks, MS Fabric, Azure Data Factory.
• Designing data flows using medallion architecture and supporting data migrations from legacy systems.
• Conducting data assessments with Data Modelers/Analysts and addressing data quality issues.
• Working with senior stakeholders to gather requirements and support modern data architecture design.
• Supporting Data Governance activities, especially around MDM and Data Quality.
• Understanding Data Science/Analytics use cases in industries like Consumer Goods, Retail, Manufacturing, or Financial Services.

Apply if you have:

• ~3 years of experience in Data Engineering or related fields (BI, Data Science).
• Hands‑on experience with Databricks, Snowflake, BigQuery, MS Fabric.
• Experience building data warehouses/lakehouses with Azure Data Factory / Synapse.
• Skills in PySpark/Python and SQL.
• Some exposure to CI/CD (Azure DevOps, Jira).
• B2+ English and ability to prepare technical documentation.
• Experience using modern tech and AI tools in daily work.

Nice to have:

• Cloud certifications (Azure/AWS/GCP) and Data Governance certifications.
• Training via platforms like Coursera/Udemy/Datacamp.
• Knowledge of data modelling (e.g., Kimball), Data Strategy, Data Quality, and MDM.
• Experience with Agile (Scrum), ML/GenAI use cases, or Agentic AI for data automation.
• Work with senior stakeholders (e.g., CDOs) in Manufacturing, Consumer Goods, or Financial Services.

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

A leading organization in the media streaming industry focuses on delivering innovative solutions that enhance user engagement and content accessibility. The company caters to a diverse audience, including individual consumers and content creators, providing them with advanced tools for content production and distribution. With a strong emphasis on technology, the organization leverages cutting-edge AI and machine learning capabilities to optimize user experiences and operational efficiency. Their commitment to continuous improvement and adaptation positions them as a significant player in the evolving media landscape.

We are looking for an experienced Data Engineer to participate in desing and architectute of client's data platform and GenAI initiatives. The primary focus of this role is building scalable distributed data processing systems that will serve as the foundation for the company's intelligent services.



Job Description

Required:

• Bachelor’s/Master’s degree in Computer Science, Computer Engineering, Machine Learning, or a related field.
• 4+ years of experience in software development, focusing on Python programming and data engineering.
• Strong expertise in Data Lake architecture and modern data warehousing.
• Hands-on experience with distributed computing systems (Apache Spark is a must).
• Strong Python proficiency for building complex pipelines and internal tooling.
• Good understanding of distributed systems principles, real-time data processing, and batch processing.
• Experience in scaling data infrastructure (AWS/GCP cloud solutions).
• Proven experience in designing and scaling vector databases (e.g. Pinecone, Milvus, Weaviate, pgvector etc.), experience in developing recommendation systems and retrieval-augmented generation (RAG) pipelines.
• Ability to independently drive technical discussions, make informed architectural assumptions, and justify technology choices.
• Ability to decompose complex "from scratch" tasks without detailed specifications.

Nice to have:

• Intermediate knowledge of Airflow for workflow management in data processing.
• Generative AI: Experience with vector databases, RAG architectures, or integrating LLMs into data pipelines.
• Experience designing and building middleware platforms, REST APIs, and distributed systems at scale.
• Web Development: Basic API development skills to provide data access to other services.

Job Responsibilities

• Data Platform: Designing and maintaining high-load Spark pipelines for processing terabytes of data.
• GenAI Support: Building data infrastructure for training and operating generative models (ingestion, cleaning, vectorization).
• Architectural Contribution: Active participation in technical brainstorming, developing data quality standards, and processing protocols.
• Optimization: Identifying and resolving bottlenecks in current distributed processing systems.

Tasks:

• Design and implementation of scalable hub-and-spoke data architectures
• Processing and analysis of large datasets in Google Cloud (BigQuery)
• Development and optimization of ETL/ELT pipelines (including CDC and SCD Type 1/2)
• Building near real-time data pipelines
• Implementation of infrastructure as code (Terraform)
• Ensuring data quality, performance, and scalability
• Collaboration with data and platform teams

Requirements:

• 3–5 years of experience as a Data Engineer or in a similar role
• Strong expertise in:
• Google Cloud (BigQuery)
• SQL and Dataform
• ETL/ELT processes (CDC, SCD Type 1/2)
• Experience with:
• Terraform (Infrastructure as Code)
• Streaming technologies (Pub/Sub, Dataflow, Apache Beam)
• Python and ideally Java
• Experience handling large datasets and cloud-based architectures
• Structured and independent working style

 Project details:

• Start: April 2026
• Duration: 6 months
• Workload: Full-time
• Location: 100% remote

About the client:

Our customer is the European online car market with over 30 million monthly users, with a market presence in 18 countries. As a Senior Data Engineer (with Data Analyst skills), you will play a pivotal role in shaping the future of online car markets and enhancing the user experience for millions of car buyers and sellers.

About the Role

As a Senior Data Engineer (with Data Analyst skills), your primary task will be to extract actionable insights from our data and advise the organization on them. To this end, you will perform analyses, convince stakeholders with your presentations, or make your results structurally visible via a dashboard. You will also liaise with product managers and marketing specialists to bring the opportunities you see in the data to fruition on our platforms, or to ensure that their ideas are correctly implemented. To ensure this, you will collaborate with the lead data and client’s data teams to implement tracking, contribute ideas on how the A/B test should be conducted, and analyze the effects of the adjustments. As part of your role, you’ll help transition the existing tech stack to the sophisticated data platform of the client.

Responsibilities:

• Migrating dashboards and underpinning analytics models from (Snowflake, dbt, Qlik) to (AWS, python) data platform
• Measuring the effectiveness of a new feature on the platform
• Analyzing and advising on the customer journey (of B2B and B2C customers)
• Analyzing the effectiveness of website personalization
• Maintaining the tag management system and Snowplow data models in DBT/SQL

Requirements: 

• You have 4+ years of relevant work experience as a Data Analytics Engineer
• You are analytical, curious, and detail-oriented
• You get energy from diverse work and solving complex data questions
• You have extensive knowledge of SQL or other languages. Preferably experience with GTM, A/B testing, and dashboarding
• Strong communication skills - you can explain insights and analytical solutions to technical and non-technical stakeholders
• High motivation, proactiveness, and a genuine enthusiasm for data and product analytics

• English level - at least Upper-Intermediate, both spoken and written  

   

The complete set of technologies the clients are currently using includes:

DBT, Snowflake SQL, Big data, AWS, AWS Quick Sight, Python, Docker, GA4, Salesforce, and Qlik. If you are excited to work with large amounts of data in a robust data stack, then this is the right place!

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently looking for a Business Development Executive to join one of our clients' team. If you're looking for an exciting opportunity to grow in a innovative environment, this could be the perfect fit for you.

We are looking for a skilled Senior Data Engineer. This role, you will be responsible for building scalable data pipelines, working with large datasets, and optimizing data workflows in a cloud-based environment. You will collaborate closely with data and engineering teams to ensure efficient and reliable data processing.

Key Responsibilities

• Design, build, and maintain robust data pipelines (ETL/ELT)
• Work with Databricks for large-scale data processing using Spark and Delta Lake
• Optimize data workflows, performance, and data storage solutions
• Collaborate with cross-functional teams including data analysts, engineers, and stakeholders
• Ensure data quality, reliability, and scalability across systems
• Contribute to continuous improvement of data architecture and processes

Job requirements

1. Strong experience with Databricks (Spark / Delta Lake)
2. Proficiency in Python for data processing
3. Strong knowledge of SQL
4. Hands-on experience with ETL/ELT pipeline development
5. Experience with at least one cloud platform (AWS, Azure, or GCP)
6. Familiarity with Git and basic CI/CD practices

Nice to Have

• Experience with dbt
• Knowledge of streaming technologies (e.g., Kafka or similar)
• Experience with Terraform or Infrastructure as Code (IaC)

N-iX is a global software development service company that helps businesses across the globe create next-generation software products. Founded in 2002, we unite 2,400+ tech-savvy professionals across 40+ countries, working on impactful projects for industry leaders and Fortune 500 companies. Our expertise spans cloud, data, AI/ML, embedded software, IoT, and more, driving digital transformation across finance, manufacturing, telecom, healthcare, and other industries. Join N-iX and become part of a team where your ideas make a real impact.

 

This role is ideal for someone at the beginning of their data engineering career who wants to grow in a supportive environment. We value curiosity, a learning mindset, and the ability to ask good questions. If you’re motivated to develop your skills and become a strong Data Engineer over time, we’d be happy to help you grow with us 🚀

Responsibilities

• Support the implementation of business logic in the Data Warehouse under the guidance of senior engineers
• Assist in translating business requirements into basic data models and transformations
• Help develop, maintain, and monitor ETL pipelines using Azure Data Factory
• Participate in data loading, validation, and basic query performance optimization
• Work closely with senior team members and customer stakeholders to understand requirements and data flows
• Contribute to documentation and follow best practices in data engineering and development
• Gradually propose improvements and ideas as experience grows
  
   

Requirements

• Up to 1,5 years of experience in Data Engineering
• Basic hands-on experience with SQL and strong willingness to work with it as a core skill
• Familiarity with Microsoft Azure or strong motivation to learn Azure-based data solutions
• Understanding of relational databases and fundamentals of data modeling
• Ability to write clear and maintainable SQL queries
• Basic experience with version control systems (e.g. Git)
• Interest in data warehousing and analytical systems
• Familiarity with Agile ways of working (through coursework, internships, or first commercial experience)
• Strong analytical thinking and eagerness to learn from more experienced colleagues
  
   

Nice to Have

• Exposure to Azure Data Factory, dbt, or similar ETL tools
• Basic knowledge of Databricks
• Understanding of Supply Chain & Logistics concepts
• Any experience working with SAP data (MM or related modules)
   

About ListKit

ListKit is a B2B lead generation platform processing 825M+ person records, serving thousands of customers across DFY (done-for-you) and PLG (self-serve) segments. Our data enrichment pipeline runs on Google Cloud (Cloud Run, PubSub, Cloud Tasks, Firestore) with TypeScript/Node.js, backed by SingleStore for high-performance JOINs against our 825M+ record dataset and SQL Server for transactional order data.

We are an AI-first engineering team. Every engineer uses Claude (Anthropic) as a core development tool for architecture decisions, code review, debugging, estimation, and daily workflows. If you haven't worked with AI coding tools yet, that's fine, but you need to be excited about making it your primary way of working.

The Role

You will own the cloud-native data enrichment platform that powers ListKit's core product: turning customer search criteria into verified, enriched lead lists.

The enrichment service (TypeScript/Node.js on GCP) orchestrates the full pipeline: receiving order requests, querying 825M+ records via SingleStore stored procedures, routing records through email finder APIs (Enrow, Prospeo, LeadMagic) and email verifiers (MillionVerifier, TryKitt), tracking job lifecycle via Firestore counters, processing billing/refunds, and delivering final exports to GCS.

The integration challenge: This service must work in lockstep with our .NET backend, which manages the customer-facing order lifecycle in SQL Server. Today, these are two disconnected state machines. Your first major project is building the completion bridge so the enrichment platform's results flow back to the .NET order processing pipeline. You don't need to be a .NET developer; we have a strong .NET team. But you need to understand the integration surface and design clean APIs between the two systems.

What You Will Do

First 30 days

• Take ownership of the listkit-enrichment codebase (TypeScript/Node.js, Cloud Run)
• Understand the full enrichment flow: PubSub dispatch, Cloud Tasks per-row processing, provider waterfall, verification chains, Firestore lifecycle counters, SingleStore JOINs, billing/refund integration
• Build the completion callback: when GCP enrichment finishes, write results back to SQL Server's OrderProcessing table so the .NET pipeline detects completion
• Fix the data-retrieval PubSub subscriber gap

First 90 days

• Own the enrichment platform end-to-end: reliability, performance, monitoring, alerting
• Improve provider waterfall logic: smarter failover between Enrow, Prospeo, LeadMagic, and the SingleStore JOIN path
• Build observability: structured logging, Cloud Monitoring dashboards, SLA tracking per provider
• Reduce enrichment latency for large orders (1000+ records)
• Work with the .NET team to design the target architecture where GCP enrichment fully replaces the Windows Services

Ongoing

• Adding external email finding/verification API providers to Cloud Run/Cloud Tasks
• Optimize SingleStore stored procedures (sp_enrich_csv) for the 825M+ record dataset
• Scale the enrichment platform for higher throughput as order volume grows
• Build and maintain the external enrichment API (API key auth, rate limiting, usage tracking)
• Contribute to infrastructure-as-code (Terraform for GCP resources)

Required Skills

TypeScript/Node.js (3+ years)

• Express.js, async/await, Promises, streaming
• Building and consuming HTTP APIs at scale
• Error handling in distributed async workflows (retries, idempotency, dead-letter queues)

Google Cloud Platform (2+ years, or equivalent AWS/Azure)

• Cloud Run (containerized services, autoscaling, concurrency)
• PubSub (message queues, subscriptions, delivery guarantees)
• Cloud Tasks (HTTP task dispatch, rate limiting, retry policies)
• Firestore (document database, transactions, atomic counters)
• GCS (object storage, signed URLs, streaming reads)
• Terraform (infrastructure-as-code)

Database experience

• SQL: complex JOINs, stored procedures, query optimization against 100M+ row tables
• SingleStore, MySQL, or PostgreSQL (enrichment service uses SingleStore)
• Basic SQL Server knowledge for the integration surface

Distributed systems thinking

• State tracked across multiple stores (Firestore + SingleStore + SQL Server)
• Eventual consistency, idempotency guards, distributed locking
• Job lifecycle management: queued, processing, completed, failed
• Rate limiting against third-party APIs with different throttle characteristics

AI-first development mindset

• Experience with AI coding assistants (Claude, GitHub Copilot, Cursor, or similar)
• Willingness to use Claude as your primary tool for code review, debugging, architecture, and estimation
• We estimate all engineering work assuming AI-assisted development. Mechanical work compresses dramatically. You estimate only irreducible human work.

Nice to Have

• Experience integrating with third-party enrichment/data APIs (email finders, verifiers, data providers)
• Experience building multi-tenant SaaS APIs with API key authentication and per-plan rate limiting
• Stripe billing integration (credits, refunds, usage-based pricing)
• Understanding of .NET/C# (reading code, understanding integration points)
• Event-driven architectures and CQRS patterns
• Familiarity with Linear for project management

How We Work

• AI-first: Claude is embedded in everything we do. Code review, spec writing, debugging, Linear issue management. We track AI adoption scores and expect engineers to reach Vanguard tier.
• Async by default: Team spans Lisbon (WET/WEST), Ukraine (EET) and US. Written communication via Slack, Linear, and documented decisions.
• Ship fast, fix fast: Continuous deployment for hotfixes, scheduled release trains for features. Production incidents get addressed immediately.
• Ownership mentality: You own your systems end-to-end. If the enrichment platform breaks, you fix it.

Tools & Stack

Your primary stack

• TypeScript (Node.js 20+) on Google Cloud Run
• Google Cloud: PubSub, Cloud Tasks, Firestore, GCS, Secret Manager, Cloud Build
• SingleStore (825M+ row dataset, stored procedures, columnar JOINs)
• Terraform (infrastructure-as-code)
• Third-party APIs: Enrow, Prospeo, LeadMagic, MillionVerifier, TryKitt, AtData

Integration surface

• SQL Server (read/write to OrderProcessing table for the .NET bridge)
• .NET backend (you design the API contract; the .NET team implements their side)

Shared tooling

• Claude (Opus/Sonnet via claude.ai, Claude Code CLI, MCP connectors)
• Linear (project management with Claude-integrated workflows)
• Slack, Google Meet, Granola (meeting notes)
• GitHub Actions (CI/CD), Stripe (billing), Intercom (support), HubSpot (CRM)

How to Apply

Send your CV and a short note (3-4 sentences) answering one of:

1. "Describe a Cloud Run or serverless service you built that processes jobs asynchronously via PubSub/SQS/EventBridge. How did you handle job lifecycle, retries, and completion detection?"

2. "Tell us about a time you had to integrate two systems that tracked state in different databases. How did you design the bridge?"

We will prioritize candidates who can start within 2-3 weeks.