Top Data Engineer Jobs in Bucharest

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 high-volume Kafka ingestion pipelines, develop Spark loaders and enrichments, design and optimize Iceberg lakehouse models, create dbt/Dremio transformations, manage Kubernetes/Helm deployments and CI/CD, and implement monitoring, PII handling, and governance for a multi-tenant telecom CDP.

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, 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.

Design and build scalable data pipelines and models for scientific instrument data; implement custom parsers for diverse file formats; create unit/integration tests and utility libraries; analyze datasets to design shared data model components; produce visualizations, reports, and interactive dashboards.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate and optimize data from SQL Server sources, ensure data quality and performance, and support BI/reporting teams (Power BI). Evolve and support legacy ETL (SSIS) while contributing to modern cloud data platform design.

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.

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.

Design, build, and maintain ETL/ELT pipelines using Azure Data Factory / Microsoft Fabric to integrate APIs, databases, and files into Azure Data Lake and Azure SQL. Ensure data quality, validation, logging, monitoring, CI/CD, performance tuning, and production troubleshooting to support enterprise analytics and Power BI reporting.

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 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.

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.

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.

As a Big Data Platform Engineer, you will manage and optimize Cloudera Data Platform clusters, automate workflows, and collaborate with teams to enhance data operations.

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.

As a Senior Data Engineer, you will design and implement scalable data solutions, develop automated pipelines, and support cloud transition using AWS and Snowflake.

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, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

Performs source-to-target data mapping for banking system migrations, defining transformation, derivation, lookup, reconciliation, and loading rules. Analyzes legacy systems, profiles and cleanses data, validates mapping logic with business stakeholders and vendors, and ensures regulatory field coverage. Supports SQL transformations, migration-tool configuration, cutover planning, dry runs, go-live execution, and financial reconciliation while maintaining mapping documentation and status reporting.

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.