Top Data Engineer Jobs in Romania

Lead the design, implementation, and operation of enterprise reporting and analytics platforms. Build scalable pipelines, master data structures, lakehouse and warehouse models, Power BI semantic models, dashboards, and cost-rate databases. Ensure data quality, governance, security, lineage, and auditability while supporting DevOps, deployment, monitoring, and production operations. Partner with finance, operations, and senior stakeholders to deliver trusted data products and business outcomes across complex enterprise systems.

Design and implement cloud-native data platforms and lakehouse architectures on AWS. Build ETL/ELT pipelines, streaming systems, data services, and AI solutions including RAG and vector search. Apply data modeling, governance, orchestration, observability, and reliability practices. Collaborate with customers and internal teams on technical designs, modernization projects, troubleshooting, code reviews, performance tuning, and production support.

Designs and deploys Power BI analytics solutions, data models, dashboards, and reports. Builds and optimizes ETL pipelines, SQL and DAX queries, Azure Synapse workflows, and Azure Data Lake Storage solutions using Parquet. Contributes to large-scale backend data processing systems, data warehousing, quality testing, and platform improvements. Collaborates with cross-functional teams, documents and defends technical designs, supports Agile delivery, mentors colleagues, and promotes software engineering best practices.

Build and evolve a secure self-service platform that provisions Databricks workspaces for banking clients. Develop Terraform-based Azure infrastructure, Airflow orchestration, data synchronization pipelines, and governance integration between on-premises systems and cloud environments. Collaborate with client engineering teams, take technical ownership, and participate in quarterly on-site planning sessions in Germany.

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.

Design, build, and manage batch and real-time data pipelines on GCP. Implement Terraform IAC, CI/CD, APIs, and CDC ingestion via Datastream. Build medallion architecture with Dataform/dbt, optimize BigQuery and ClickHouse models, create Airflow DAGs with monitoring, and maintain GDPR/CCPA compliance and data governance.

The Data Engineer will build and optimize data pipelines, integrate data sources, enhance analytics platforms, and collaborate with teams to deliver data solutions.

Develop and support Qlik Sense data visualization applications, Qlik and SAP HANA data models, and data pipelines. Deliver solutions through sprint planning, manage application incidents and requests, and produce user and development documentation. Collaborate with data transport and SAP BI teams while supporting internal users in a multicultural environment.

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.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Provide hands-on Apache Airflow engineering and customer enablement: develop and troubleshoot DAGs, integrate Airflow with data ecosystems and cloud services, support production onboarding, resolve distributed system issues, deliver technical guidance and workshops, and collaborate with customers and internal teams to stabilize and operate a managed Airflow platform.

Design and build cloud-native data platforms and lakehouse architectures on AWS. Develop and maintain ETL/ELT and streaming pipelines, support RAG/vector search and ML integrations, ensure data modeling, governance, observability, troubleshooting, and migration from legacy ETL to cloud warehouses while engaging with customers and stakeholders.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Build, deploy, and maintain ETL/ELT pipelines, ensuring data quality, observability, and documentation. Collaborate with technical and business stakeholders to define use cases, support data products in production, and improve engineering practices.

Lead the architecture and delivery of a scalable enterprise data platform for high-volume AdTech workloads. Design Iceberg-based batch and near-real-time data flows, canonical data models, governed data contracts, and analytical and operational data products. Establish standards for observability, quality, lineage, security, schema evolution, testing, deployment, and cost optimization. Mentor engineers, make pragmatic greenfield technology decisions, and collaborate with platform teams and leadership to deliver a production-ready data foundation.

Design, build, and maintain data solutions using PySpark and Power BI. Own small projects end-to-end, work with business users to gather requirements, validate solutions, support testing and troubleshooting, and ensure delivered solutions meet business needs.

Build and operate production batch and streaming data pipelines from databases, event streams, and APIs. Own lake, lakehouse, and warehouse architecture, optimizing performance and cost. Implement orchestration, testing, observability, alerting, recovery, infrastructure as code, CI/CD, data contracts, governance, access controls, lineage, and documentation. Collaborate with leadership, clients, and data-producing teams while mentoring others and communicating technical decisions clearly.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Develop and maintain SCOR’s enterprise data platforms, including Databricks and Palantir Foundry. Build deployment pipelines, automation, monitoring, observability services, operational controls, reusable components, and self-service capabilities. Support platform administration, access management, configuration, governance, documentation, and issue remediation. Collaborate with data engineers, architects, and platform leads to ensure secure, scalable platforms and accelerate data delivery.

Build and maintain analytical pipelines and reporting solutions using BigQuery and MariaDB. Develop Python, JavaScript, FastAPI, and AI-powered tooling; write advanced SQL; document data architecture and lineage; apply AI/ML to analytics; and translate findings into business decisions. The role also involves process analysis, stakeholder communication, automation, and end-to-end ownership of scalable data infrastructure and insights.

Administer and optimize mission-critical Oracle 19c/21c databases on Exadata. Responsibilities include RAC, ASM, Data Guard, RMAN, performance diagnostics, storage and networking administration, patching, upgrades, troubleshooting, automation, incident resolution, documentation, and compliance support. The role collaborates with engineering and application teams to improve reliability, availability, and performance across the full Exadata stack.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

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 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.