Senior Data Engineer

O projekcie:
We are seeking a Senior Data Engineer to lead the design and implementation of scalable data pipelines using Databricks, Snowflake, cloud-native services, and distributed data processing frameworks. You will work across various cloud platforms (AWS, GCP, Azure), contributing to architecture decisions and the adoption of lakehouse patterns. The role involves technical leadership on client projects, ensuring adherence to best practices in data modeling, pipeline orchestration, security, and performance optimization.

WHAT WE OFFER:

- Competitive compensation packages.
- Stable employment, based on a full-time employment contract.
- Private health insurance (Medicare Сlinic).
- AYCM sport pass, providing discounts at various sports facilities in Hungary.
- Interesting tasks and diverse opportunities for developing your skills.
- Free training courses.
- Participation in internal and external thematic events, technical conferences.
- A spacious office in the heart of Budapest (13th district).
- All necessary devices and tools for your work.
- Active corporate life.
- The friendly and supportive atmosphere within the team.

If you feel you’re ready to join the team, apply for this job now! We’re already looking forward to meeting you!

Wymagania:
The Tech Stack

- Core Languages: Python or Scala (Expert/Patterns), SQL (Expert/Internals).
- Compute & Storage: Databricks (Unity Catalog), Snowflake, BigQuery, Synapse.
- Processing: Spark/PySpark (Deep internals, Tuning, Streaming), dbt (Enterprise patterns).
- Architecture Patterns: Data Mesh, Lakehouse (Delta Lake/Iceberg), Lambda/Kappa.
- Infrastructure & DevOps: Advanced Terraform/IaC, CI/CD, Docker/Kubernetes.
- Emerging Tech: Feature Stores, Vector Databases, MLOps basics.

Your Skills

- Engineering Mastery: Expert-level proficiency in Python (design patterns, library development) and SQL. Ability to optimize code others wrote.
- Deep Platform Expertise: Mastery of Databricks (Spark memory management, partitioning strategies) or Snowflake (Warehouse tuning, RBAC, Zero-Copy Cloning). You understand how they work under the hood.
- Architectural Vision: Ability to design end-to-end data solutions, select the right tools (e.g., “Why Snowflake over Redshift?”), and defend decisions to client leadership.
- Cloud Mastery: Expert-level knowledge of AWS, GCP, or Azure. Deep understanding of networking (VPC, PrivateLink), security (IAM), and integration limits.
- Consulting & Business: Experience participating in Presales, technical audits, or discovery phases. Translating business needs into technical specs.
- AI/ML Readiness: Understanding of engineering data for Machine Learning (Feature Engineering, pipelines for LLM/RAG).

Nice to Have

- Streaming: Deep experience with Kafka, Kinesis, or Spark Structured Streaming.
- GenAI Stack: Experience with Vector Databases (Pinecone, pgvector, Weaviate) or frameworks like LangChain.
- Certifications: Professional-level cloud certifications (e.g., AWS Solutions Architect Pro, Databricks Certified DE Professional).
- NoSQL: Advanced modeling for DynamoDB, Cosmos DB, or MongoDB.

Codzienne zadania:
- Architecture & Leadership: Lead the design and implementation of scalable data pipelines and Lakehouse architectures. Act as the Design Authority.
- Advanced Engineering: Solve the hardest technical challenges—optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks.
- Consulting & Presales: Participate in technical assessments, audits of existing systems, and proposal estimations. Explain the ROI of technical modernization.
- Performance & Security: Ensure all solutions are production-ready: secure, monitored, cost-efficient (FinOps), and documented.
- Mentorship: Define coding standards, conduct code reviews, and mentor Middle/Junior engineers to foster a culture of engineering excellence.