• Design, develop, and maintain data pipelines using Apache NiFi, ensuring efficient ingestion and transformation. • Integrate structured and unstructured data from multiple sources, including Salesforce, APIs, SFTP, SAP, and CRM, into cloud-based and on-premise databases. • Ensure data quality, security, and governance across diverse sources and destinations. • Optimise and troubleshoot NiFi workflows to improve performance, reliability, and scalability. • Work with APIs to fetch and send real-time data for external integrations (e.g., CustomerGauge). • Manage and operate NiFi architecture within Kubernetes environments. • Collaborate with data scientists, analysts, and engineers to ensure data availability and usability. • Improve and optimise ETL/ELT processes for large-scale data processing. • Use NiFi Registry and Toolkit to maintain version control and streamline deployments across environments. • Develop Bash and Python scripts for automation, monitoring, and workflow execution. • Implement CI/CD pipelines for data pipeline deployments and workflow automation. • Analyse and resolve data pipeline issues, ensuring high availability and reliability.

Title: Principal AI Data Engineer Location: Sofia Bulgaria Full time Remote   Thermo Fisher CRG has an exciting opportunity within our CRG Digital group. We are looking for a Principal AI Data Engineer to join our team.   In this role, you will work directly with stakeholders to understand their needs, and design and build data product solutions that enable a range of use cases, including for AI agents, to reliably access, reason over, and act on enterprise and clinical data   The focus is on turning raw data into use case-ready data products using Snowflake, SQL and modern AI tooling. This is not a model development or research position. The ideal candidate enjoys working deeply with data, profiling, shaping and structuring it, so that downstream systems, including AI agents, can use it effectively   Essential Functions • Experienced in working directly with non-technical stakeholders and leading technical projects. • The successful candidate will need to be data proficient - likes to use data to make decisions as well as technically intuitive and enjoy learning new technology. • User-focused - Keeps user needs in mind. Thoughtful about what’s intuitive for others, what’s not and why. • Thorough knowledge of SQL and a range of technologies including one or more of the following: Snowflake, Databricks, MCP Servers, Microsoft Power Platform (e.g. Power Apps, Power BI), ETL, Python, and/or R. • Understands how data structure, quality and context impact downstream AI/LLM use cases • Direct knowledge of LLM, Agentic AI, Machine Learning and AI Engineering techniques an advantage - Familiarity with AI/LLM concepts is a plus, particularly in how data preparation and structure influence AI outputs (no model development required) • Generates and tests hypotheses and analyzes and interprets the results. • Navigates large, complex datasets for shaping, profiling, and curation, as well as identifies related data that is fundamental to successfully applying predictive techniques. • Understanding of relational database and experience working with complex data systems. • Designs, develops and programs methods, processes, and software programs to consolidate, cleanse, and analyze unstructured, diverse data sources to recognize patterns, identify opportunities, and generate actionable business insights and solutions. • Understands how data structure, lineage, and quality impact downstream AI/LLM use cases, Collaborates with AI/ML teams but does not focus on model development. • Identifies meaningful insights from large data and metadata sources in support of continuous improvement efforts and business process upgrades through exploratory data analysis. • Ability to work on a multi-disciplinary project team • Excellent problem solving and innovative skills • Excellent written and verbal communications skills • Ability to effectively organize multiple assignments with challenging timelines • Ability to adapt and adjust to changing priorities • Demonstrated positive attitude, enthusiasm toward work, and the ability to work well with others   Education and Experience:   • Bachelor's degree in computer science, statistics, biostatistics, mathematics or related field or equivalent and relevant formal academic / vocational qualification, and at least 5 years of experience that provides the knowledge, skills, and abilities to perform the job requirements.   Required Knowledge, Skills and Abilities: - Data proficient - likes to use data to make decisions - Technically intuitive – likes learning new technology tools and able to do so quickly - Thorough knowledge of SQL and a range of technologies including one or more of the following: Snowflake, Databricks, MCP Servers, Microsoft Power Platform (e.g. Power Apps, Power BI), ETL, Python, and/or R. - Understands how data structure, quality and context impact downstream AI/LLM use cases - User-focused - Keeps user needs in mind. Thoughtful about what’s intuitive for others, what’s not and why - Experience demonstrating a strong attention to detail - Ability to work on a multi-disciplinary project team - Excellent problem solving and innovative skills - Excellent written and verbal communications skills - Ability to effectively organize multiple assignments with challenging timelines - Ability to adapt and adjust to changing priorities - Demonstrated positive attitude, enthusiasm toward work, and the ability to work well with others - Understanding of relational data base and experience working with complex data systems   Preferred Knowledge, Skills and Abilities: - Clinical trial experience - Ability to train, mentor and supervise others

Role Description We are a growing gaming startup building data-driven products, and we’re looking for a mid-level Data Engineer to help build, maintain, and scale our data and analytics platform. This is a hands-on role where you’ll work closely with analytics, product, and game teams to turn raw game and product data into reliable, well-modeled datasets that support decision-making across the company. Qualifications - Hands-on experience with dbt (models, tests, documentation) - Experience working with Google BigQuery - Solid Python skills for data engineering use cases - Strong SQL skills and understanding of data modeling concepts - Experience building and maintaining production data pipelines - Familiarity with Git and collaborative development workflows - Strong English capabilities, both written and verbal - Good communication skills and the ability to work effectively with cross-functional teams - A serious, responsible mindset — takes ownership and accountability for work - High motivation to succeed, learn, and grow in a startup environment Requirements - Experience in the gaming industry (mobile, F2P, online, or real-time analytics) - Understanding of event-based data, funnels, retention, cohorts, and LTV - Experience with data orchestration tools (Airflow, Prefect, Dagster) - Familiarity with data ingestion tools (Fivetran, Airbyte, or similar) - Exposure to GCP beyond BigQuery - Experience working in a startup or fast-paced environment Benefits - Build and maintain ELT data pipelines for game and product data - Develop, test, and document dbt models following analytics engineering best practices - Work extensively with Google BigQuery for analytics, optimization, and cost efficiency - Write clean, maintainable Python for data processing and automation - Collaborate closely with analytics, product, and game teams — clearly communicating requirements, assumptions, and outcomes - Ensure data quality, reliability, and consistency across datasets - Monitor pipelines, troubleshoot issues, and take ownership of fixes end-to-end - Contribute to data architecture decisions in a fast-paced startup environment Company Description MWDN is an international recruitment and outstaffing company with 23+ years of experience connecting exceptional tech talent with leading companies across Israel, the USA, Great Britain, and Western Europe. In this role, MWDN acts as the recruitment partner, and the selected candidate will work directly with the client’s team on their product in an international environment.

• Develop and maintain robust ELT pipelines and transformation models. • Support and optimise our Snowflake data warehouse performance. • Build and maintain dbt models, ensuring associated documentation is current. • Monitor and troubleshoot failures across data pipelines, Airbyte connectors, schema changes, and ingestion issues. • Support critical Tableau reporting datasets and meet downstream reporting requirements. • Drive improvements in reliability, governance, and observability across the entire data platform. • Support Finance and commercial reporting needs, including revenue recognition, P&L, and margin analysis. • Work closely with stakeholders across Finance, Commercial, Product, and Operations. • Contribute actively to initiatives focused on data quality, metric consistency, and documentation.