Top Data Engineer Jobs in Romania

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Build and maintain cloud-based data lakehouse pipelines using SQL-driven transformations. Harmonize, model, and prepare data for analysis, reporting and AI. Ensure data availability, integrity, security, monitoring and performance. Collaborate with cross-functional teams, share knowledge, and adopt modern Data Engineering and DevOps practices.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design, build, and maintain scalable batch and real-time data pipelines, ELT workflows, cloud data infrastructure, orchestration, and streaming solutions. Ensure production reliability, performance, security, governance, monitoring, and data quality while collaborating with technical and business stakeholders. Participate in on-call support and continuous improvement initiatives.

Build scalable pipelines to ingest and process high-volume unstructured insurance documents, including PDFs, scans, emails, Word, Excel, and PowerPoint files. Responsibilities include OCR integration, document parsing, text normalization, semantic chunking, metadata extraction, enterprise connectors, retrieval-ready data preparation for RAG systems, validation, monitoring, logging, testing, and secure cloud deployment using software engineering best practices.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Develop and maintain ETL/ELT processes, data platforms, and big-data pipelines that feed live European operations. Treat data as production code, enable data modeling and orchestration, and can grow into ML engineering within an AI track.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.

As a Consultant at Deloitte, you'll design and implement technology solutions for large companies, driving business value through innovative software development.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Lead PostgreSQL database engineering and bulk data management across BSS/OSS/NOC stacks. Build backup/DR, tuning, bulk import/export pipelines, monitoring, multi-datacenter replication, data governance, and runbooks. Support integration with network-element databases and advise on schema and access controls.

The Senior Data Engineer will develop scalable ETL pipelines, write maintainable Python code, optimize data transformation processes, and utilize Google Cloud services while collaborating in an agile team.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Principal Engineer, you'll design and evolve Masabi's platform architecture, deliver scalable services, guide technical decisions, and drive improvements across engineering teams.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Build and deploy data products on an AWS data mesh platform, onboard data from legacy warehouses, and develop streaming and batch pipelines using Flink, SQL, Python, Kafka, Iceberg, and DynamoDB. Collaborate with product engineering teams on data contracts, ownership, documentation, and platform adoption. Provide hands-on enablement, debugging support, technical guidance, and leadership for end-to-end initiatives.

Develop and maintain low-latency data platform components and feed handlers using Java; implement automated testing and monitoring; collaborate with Product Owner and cross-border teams; learn KDB/q and financial markets concepts.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.