Top Data Engineer Jobs in Bucharest

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

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.

Assess and redesign multi-store reporting architecture; design scalable, high-performance analytics and real-time/near-real-time reporting solutions; define data modeling and event enrichment strategies; implement efficient unique-count and aggregation approaches; recommend technologies and trade-offs; support migration and ensure reporting consistency; drive data quality, observability, and performance optimizations; mentor and guide engineering teams.

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 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 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.

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.

Own and improve customer and product data pipelines, CDP and analytics platforms (Segment, Mixpanel, BigQuery, dbt). Build analytics-ready models, replace fragile integrations, enforce data governance, QA, and monitoring, and partner with engineering, product, marketing, and sales to enable downstream reporting and activation.

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.

Design and implement backend systems, ETL pipelines, data storage, and APIs for a SaaS platform. Maintain platform availability and improve security, quality, testing, automation, documentation, and features. Support customers and staff, contribute to sprint planning and prioritization, estimate development effort, track project progress, and proactively resolve roadblocks.

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

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.

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.

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.

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.

Design, implement, and manage scalable cloud-native batch and real-time data pipelines. Process structured and unstructured data, optimize ETL/ELT and data models, evaluate new tools, collaborate with data scientists and analysts, automate Unix-based operations, and document best practices.

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.

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.