Top Data Engineer Jobs in Romania

Design, build, and maintain low-latency, large-scale backend APIs and data pipelines to ingest, enrich, and manage product catalog data. Ensure high performance (SLAs <45ms), scalability for billions of daily requests, integrate privacy proposals, collaborate across teams, and follow best practices for testing and documentation.

Build and operate robust, scalable backend data pipelines processing terabytes of data; contribute to technical roadmap and architecture; deploy and optimize systems for reliability and performance; collaborate with product and cross-functional teams in Agile; mentor junior engineers.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.

Lead Finance data engineering: own end-to-end pipelines for financial and regulatory processes (IFRS 17), ensure scalability, auditability, quality and governance, design large-scale processing, document lineage and models, provide hands-on technical leadership, mentor engineers, and collaborate with finance, actuarial, risk and AI stakeholders to evolve data engineering practices for analytics and AI.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Lead delivery of data and content migration projects, implement data governance and quality solutions, review and improve migration tools, provide technical plans and troubleshooting, act as SME for content migrations, and manage risks across multiple projects while collaborating with clients and delivery teams.

The role involves driving AI tooling adoption, developing AI agents, embedding in Scrum teams, and optimizing AI's impact on engineering productivity.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Design, build, and operate production-grade Databricks-based data pipelines across medallion architecture. Define canonical data models, ensure performance, quality, governance, and reliability. Collaborate with data scientists and stakeholders, build data integrations/APIs, mentor engineers, and continuously optimize data infrastructure and workflows.

The Qlik Sense Developer will create applications, manage data modeling, support internal users, and collaborate with teams focused on data visualization.

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Lead design, build and maintain scalable data pipelines using Azure Data Factory, Databricks and SQL Server. Develop Python (PySpark) automation and data processing, optimize data models and queries, resolve pipeline issues, improve data quality and resilience, collaborate with cross-functional teams, and mentor junior engineers while establishing engineering and governance best practices.

Build and scale ad-tech data infrastructure to ingest, normalize, and transform massive log-level and reporting data from SSPs, DSPs, and ad servers. Design ETL/ELT pipelines, data models, and warehouses optimized for real-time analytics, implement data quality monitoring, and support BI and analytics teams while ensuring GDPR/CCPA compliance.

Maintain and scale production data ingestion pipelines processing billions of events, support and optimize complex ETL and streaming workflows, develop platform tools and ML/streaming initiatives, ensure reliability/observability, and assist data scientists and analysts with production workloads.

Lead and deliver end-to-end corporate data pipelines and analytical datasets, ensuring reliability, scalability, data quality, lineage, and governance. Provide hands-on technical leadership, design large-scale data processing solutions, coach data engineers, collaborate with business stakeholders across group functions, and drive enterprise data standards and AI-ready practices to support corporate reporting, KPIs, and regulatory needs.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead design, build, and operate scalable lakehouse/warehouse data platforms and pipelines for analytics and ML. Own end-to-end ingestion, transformation, storage, governance, DevOps/DataOps, and delivery. Drive architecture, tooling, cost/performance optimization, mentor engineers, engage in presales, and partner with stakeholders to deliver reliable, secure data solutions.

Design, build, and maintain scalable real-time and batch data pipelines for programmatic ad-tech. Ingest and normalize SSP/DSP/GAM data, develop ETL/ELT workflows, data models for yield and auction analytics, maintain warehouses/lakes, implement data quality monitoring, and ensure GDPR/CCPA compliance while supporting BI and analytics teams.

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

Design and implement scalable Azure data platforms, develop data pipelines, and support clients with data modernization and governance practices.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Design, build, and operate Azure-based data platform environments and lead migrations to Azure/Databricks/Microsoft Fabric. Implement IaC (Terraform), Kubernetes platform operations, CI/CD automation, security and governance controls, monitoring/observability, and cost/performance optimizations. Collaborate with data engineers, architects, and stakeholders and produce technical documentation while providing technical leadership during the migration.

Build and optimize scalable ETL/ELT pipelines, design data models, ensure data quality and security, improve pipeline/query performance, implement CI/CD for data workflows, and collaborate with product and analytics teams to enable data-driven decisions.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.