Top Data Engineer Jobs in Hungary

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Design and develop scalable Databricks Lakehouse solutions across major cloud platforms. Build Delta Lake pipelines, data models, governance layers, and secure access using Unity Catalog. Write SQL and Python, automate deployments with DABs and CI/CD, and orchestrate workflows. Manage technical delivery components, estimation, testing, documentation, and stakeholder collaboration while leveraging cloud-native storage and security services.

Build and support high-volume real-time data pipelines using Amazon Kinesis, Databricks, Spark Structured Streaming, and Delta tables. Optimize pipelines for performance, latency, reliability, and cost; implement CloudWatch monitoring and alerting; integrate streaming and batch systems; write testable Python and SQL; and troubleshoot data-processing issues. Candidates must be Australian citizens with an active security clearance.

Build and administer secure, scalable Databricks environments on AWS. Responsibilities include configuring Unity Catalog and governance controls, designing VPC and PrivateLink network architectures, managing IAM and access models, developing production data pipelines, implementing Terraform infrastructure as code, establishing CI/CD deployments, and enforcing cluster policies and platform standards.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Design, build and maintain scalable ELT/ETL data pipelines and the Data & Analytics platform. Integrate and transform internal and external sources, define logical and physical data models, ensure high data quality, monitor and resolve pipeline incidents, document processes, and advise on architecture and best practices including lakehouse and big data capabilities.

Build and operate production-grade Kafka-compatible streaming systems and Apache Flink applications. Develop Go services, manage event processing, state, checkpoints, schemas, and delivery guarantees, and deploy workloads on AWS and Kubernetes. Troubleshoot distributed production systems while improving reliability, scalability, performance, and resilience. Collaborate with engineers on platform evolution and apply AI-powered development tools and GenAI workflows.

Design and evolve Azure-based Lakehouse platforms using Databricks, Delta Lake, Data Factory, and related services. Develop sustainable architectures, prototype technologies, support engineering teams, and establish platform principles and best practices. Ensure secure handling of business-critical and personal data while contributing to data warehouse modernization, migration, streaming pipelines, analytics, and organizational structures.

Build and maintain high-performance data lakehouse infrastructure, including Apache Iceberg maintenance workflows, compaction, snapshot expiry, manifest rewriting, and orphan-file cleanup. Develop Rust-based query-routing systems, maintain connectors, review open-source contributions, and ensure data integrity under concurrency, retries, and failures. Operate production systems, participate in on-call support, debug customer environments, and contribute to product roadmap decisions.

Operate and maintain the Power Platform frontend of an Azure-based Cloud Data and Analytics Platform. Responsibilities include managing Power BI, Power Apps, Power Automate, SharePoint, gateways, permissions, refreshes, monitoring, incidents, governance, and compliance. The role coaches self-service BI teams, supports onboarding, documents standards, evaluates new capabilities, automates operations, and aligns Power Platform services with Azure Lakehouse technologies and data engineering practices.

Manage and migrate ETL pipelines across source systems, validate pipeline builds and data quality, resolve pipeline issues and discrepancies, and support data migrations. Provide operational support in Snowflake, collaborate with business stakeholders and subject matter experts, document work, and maintain data engineering best practices. The role requires SQL, Python, Pandas or PySpark, advanced Excel, analytical problem-solving, and familiarity with modern data engineering tools.

Design and implement modern data architectures and lakehouse solutions using cloud platforms and tools such as Databricks and Snowflake. Provide technical leadership, stakeholder collaboration, pre-sales expertise, effort estimates, team mentoring, and code reviews. The role involves long-term project-based consulting with flexible full-time or part-time engagement and occasional onsite client work.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Senior Data Engineer responsible for designing, maintaining, and improving reliable, scalable data infrastructure and production pipelines. The role includes data lake development, orchestration, data quality monitoring, testing, troubleshooting, performance optimization, and architectural contributions. The engineer will collaborate with Data, Platform, Infrastructure, DevOps, and Software Engineering teams to deliver production-grade data systems supporting analytics, reporting, and AI-enabled products.

Lead design and development of backend APIs and front-end components for a Risk & Analytics platform. Implement FO/risk requirements, code, test, debug, monitor the platform, collaborate across teams, and mentor junior engineers. Support data visualization and integrate financial models and data pipelines for risk management and asset valuation.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Staff Data Engineer responsible for shaping data architecture, building and optimizing production pipelines, designing data lake solutions, and improving data quality, observability, scalability, and reliability. The role is a hands-on technical leadership position requiring architectural ownership, cross-functional collaboration, and delivery of complex data systems from design through production using Python, SQL, AWS, and modern data technologies.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Senior Data Engineer responsible for aligning data solutions with business needs, architecting data models and cloud lakehouse platforms, leading technical delivery and testing, supporting pre-sales estimation and proposals, and mentoring project teams. The role requires consultancy experience, technical leadership, strong communication in English and Hungarian, and hands-on expertise with Databricks, Snowflake, Azure, or AWS data platforms.

Provide technical support to customers using SAP CDC and CDP platforms. Troubleshoot issues, identify root causes, document solutions, maintain knowledge content, manage support cases within SLA, and collaborate with internal teams and third parties. The role also involves supporting improvement projects, learning CDC APIs and integrations, and using AI tools and agentic workflows to improve support quality and efficiency.

Owns the architecture, development, and optimization of high-throughput batch and streaming data pipelines supporting maritime trade intelligence. Responsibilities include using Python, Spark, Kafka, and Airflow; managing AWS infrastructure with Terraform and Docker; ensuring data quality, reliability, monitoring, and SLA performance; optimizing costs; and collaborating with Product, SRE, Platform Engineering, and Data Science teams.

Develops and governs clinical data standards and study specifications aligned with CDISC and regulatory guidance. Partners with statisticians and programmers to create compliance and productivity tools, mentors teams, monitors standards and regulatory updates, supports submissions, and represents the organization in industry standards initiatives. Requires extensive programming, clinical trial data, regulatory submission, mentoring, and CDISC expertise.

The Data Engineer establishes data feeds with banks and vendors, evaluates APIs, builds and automates data pipelines, and centralizes integration processes. The role also involves migrating investment data, supporting analyses, answering data-driven questions, and collaborating across the firm. Candidates need SQL and Python proficiency, strong prioritization skills, and an interest in modern AI tools. Experience with Snowflake, Databricks, Airflow, or GCP is advantageous.