Top Data Engineer Jobs in Hungary

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.

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.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

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.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

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.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Designs, develops, and optimizes scalable batch and streaming data pipelines using Azure Databricks, Spark, Delta Lake, Azure Data Factory, and related Azure services. Responsibilities include data modeling, performance tuning, cluster management, monitoring, governance, security, data quality, CI/CD, documentation, and collaboration with analytics and data science teams. The role also supports machine learning integration, business intelligence, and migration to Databricks as a strategic data platform.

Support development of AI and perception solutions for camera and radar products used in autonomous driving and safety-critical vehicle systems. The intern will work with innovative software technologies, collaborate in an agile international team, and receive mentoring while contributing to autonomous mobility projects.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Design and maintain scalable data transformation pipelines and analytics-ready Snowflake models using Coalesce.io. Model complex workforce data, including employee lifecycles, organizational hierarchies, recruiting, effective-dated records, and slowly changing dimensions. Implement data quality checks, optimize SQL and query performance, support secure data consumption, and partner with integration, platform, HR, and People Analytics teams. Maintain documentation and development workflows through Azure DevOps, GitHub, and CI/CD practices.

Develop and maintain React-based analytics dashboards and visualization tools. Build reusable TypeScript components, integrate REST and GraphQL APIs, apply data visualization libraries, optimize performance, and collaborate with finance stakeholders to transform complex data into actionable insights. Responsibilities also include code reviews, testing, documentation, and maintaining scalable front-end architecture.

Lead development of Data and AI features from data architecture to optimization and simulation. Design scalable data architectures, reliable pipelines, and DataOps/DevOps workflows. Partner with analysts, product managers and supply chain specialists to turn complex logistics data into actionable solutions.

Lead the architecture and hands-on delivery of an Everything-as-Code data mesh, including Kafka and NiFi event streaming, Kubernetes infrastructure, Terraform and GitOps deployment, schema governance, hybrid lakehouse integration, FinOps optimization, resiliency, and distributed observability across multi-cloud and on-premises environments.

Build and optimize data architectures, databases, warehouses, lakes, lakehouses, ETL processes, and data pipelines. Write SQL and Python, integrate data sources, develop Power BI and Superset reports and dashboards, and evaluate data technologies. Monitor performance, troubleshoot issues, ensure data security and compliance, and deploy infrastructure-as-code and Jenkins-based CI/CD automation while collaborating with engineering teams.

Design and maintain scalable data pipelines for structured and unstructured data supporting analytics, automation, and AI initiatives. Build data models, integrations, and quality controls; ensure reliable, secure, governed data across systems. Partner with AI engineers, product owners, and business teams to translate requirements into technical solutions. Monitor pipeline health, troubleshoot issues, improve performance, and support metadata, lineage, and documentation practices.

Design, build, and maintain scalable, production-grade data pipelines and models (ingest, transform, deliver) to enable BI, analytics, and AI. Translate business requirements from manufacturing, procurement, and supply chain into reusable data products, ensure data quality, monitor performance, manage deployments, use Git and AI-assisted tools, and collaborate with architects and downstream teams.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

Lead technical design and improvements for a large-scale identity data platform. Prototype and evaluate pipeline changes for cost, performance, and reliability; build consensus and drive implementation; mentor engineers; integrate with broader Mastercard systems and support ML workflows.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Develops data-driven quality improvement strategies by analyzing manufacturing and field performance data, building dashboards and reporting solutions, conducting statistical analysis, and leading root cause investigations. The role supports corrective actions, audits, process capability, product performance, and cross-functional quality initiatives while translating complex findings into practical business improvements.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.