Senior Data Engineer supporting AI - enabled financial compliance initiative with data pipelines and ingestion processes. Collaborating with diverse teams in a mission - critical regulated environment.
About the role
- Senior Data Engineer architecting and building intelligent data ecosystems that integrate Generative AI. Focused on delivering high-impact solutions across diverse industries in Poland.
Responsibilities
- 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
Requirements
- 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 both Lithuanian and English languages, written and spoken.
Benefits
- 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
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