Analyze and optimize business processes by collaborating with stakeholders to uncover inefficiencies and define data requirements for automation
Design scalable, modular data architectures that integrate with Generative AI and Agentic AI systems to support real-time decision-making
Engineer robust ETL/ELT pipelines using Python, cloud-native services, and orchestration tools, supporting both batch and streaming data needs
Architect RAG and vector database solutions using semantic search to enable LLMs to retrieve curated, context-rich business data
Build intelligent data products, from predictive models and decision engines to AI-driven insights platforms
Implement data quality, validation, and governance frameworks to ensure data integrity, lineage, and compliance across systems
Lead technical discovery sessions with clients to transform complex business challenges into AI and data-driven opportunities
Mentor team members on best practices in data engineering, AI integration, and modern cloud architectures

Expert-level Python proficiency for data engineering, including API integrations, data transformations (Pandas, PySpark), and automation
Proven experience designing and deploying large-scale data platforms on AWS, GCP, or Azure
Strong foundation in building production-grade ETL/ELT pipelines using Apache Airflow, Kafka, Spark, or cloud-native tools
Hands-on experience with vector databases (e.g., Pinecone, Weaviate, Chroma, Milvus) and implementing semantic search
Demonstrated knowledge of Generative AI and LLMs, with practical experience in RAG architectures and prompt engineering
Deep understanding of data governance, quality, and documentation, with a focus on lineage, metadata, and compliance
Familiarity with cloud services including serverless computing, managed databases, and data warehouses such as BigQuery, Redshift, or Snowflake
Experience working with complex real-world data environments, including legacy systems, SaaS integrations, APIs, and databases
Fluency in Lithuanian and English, both written and spoken

A working culture that is high performing, ambitious, collaborative and fun
Health insurance and a yearly training budget (local and international conferences, language courses), employee-led workshops
Flexible working hours
Unlimited WFH (work from home) policy
Extra vacation days: 2 after working at NFQ for two years and 4 after four years on our team
Bonus for referrals
For those who dream of traveling: WFA (work from anywhere) possibilities in NFQ
approved countries
Office perks and team activities

Senior Data Engineer

Key skills