Top Data Engineer Jobs in Romania

Operate and support Kafka clusters (MSK and on-prem), manage Kafka Connect ecosystem (Debezium, KSQL, Schema Registry), troubleshoot producers/consumers, implement HA/backup strategies, automate provisioning (IaC/scripting), monitor and tune cluster performance, support incident response and on-call, and document operational procedures.

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.

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.

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

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.

Design and build a cloud-native data platform using Snowflake, dbt, Fivetran, and AWS. Migrate legacy SQL Server/SSIS/SSAS systems, develop and optimize ELT pipelines and SQL models, implement data quality, governance, monitoring and lineage, automate workflows with Git and CI/CD, and collaborate with global stakeholders to enable analytics and AI use cases.

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.

As a Big Data Platform Engineer, you'll manage and optimize Cloudera Data Platform clusters, ensuring performance and scalability for ING Wholesale Banking's data infrastructure, while automating operations and supporting collaboration across data engineering teams.

Design, build, and maintain scalable cloud-based data pipelines and ETL processes on Azure (Databricks, ADF, Synapse, ADLS). Implement data models, quality checks, governance, and performance optimization. Collaborate with architects, cloud engineers, data scientists, and stakeholders to consolidate legacy services and enable data-driven insights.

The role involves sourcing audio data for ingestion, managing cloud infrastructure on GCP, and collaborating with AI scientists to enhance data quality and efficiency.

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.

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.

The Senior Data Analytics Engineer will design and maintain analytical pipelines, write SQL for large datasets, utilize AI tools, and communicate insights to stakeholders.

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

The Software Engineer will manage data collection for model training, operate cloud infrastructure for ingestion, and collaborate on dataset roadmaps for AI teams.

Design, build, and maintain scalable AWS-based data platforms and pipelines ingesting from DynamoDB, Aurora PostgreSQL, and Neptune; implement Iceberg-based S3 data lake zones; develop CDC and batch ingestion, Airflow orchestration, Glue Data Catalog integration, CI/CD, automated testing, and monitoring; optimize Iceberg table design and troubleshoot production issues.

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.

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.

Operate and automate the data plane for a sovereign Google Cloud region: manage enterprise storage (SAN/FC/iSCSI), PostgreSQL at scale, Kubernetes CSI/stateful workloads, backup/DR pipelines, and infrastructure automation. Drive durability, availability, recoverability, incident response, and capacity planning while participating in follow-the-sun on-call with Google Cloud SRE teams.

Design and deliver analytics-ready datasets and data products for a business domain (P&C, L&H, or Finance). Translate business needs into data models, ensure data quality and governance, document lineage and metrics, and collaborate with stakeholders and platform teams to enable discoverable, self-service analytics.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design, build, and maintain large-scale cloud-native data pipelines and lakehouse/warehouse solutions using Databricks, Spark/PySpark, Delta Lake and Snowflake. Collaborate with architects, product owners and data teams to implement ETL, data quality, governance, MLOps (MLflow) and BI integrations while ensuring best-practice CI/CD, version control and operational reliability.

Conduct 60-minute technical interviews for senior data engineering candidates, evaluate cloud-based data platform architecture, ETL/ELT pipelines, distributed processing, security, data modelling, and communication. Complete scorecards, provide hiring recommendations, and collaborate with recruiters to improve assessments.

As a Senior DataOPS Engineer, you will architect and maintain reliable data platforms, ensuring performance and compliance while mentoring engineers and optimizing workflows across the organization.

Design and operate scalable, near-real-time data pipelines and backend services for a global payments platform. Model transactional ledger data for reconciliation, ensure data quality and observability, and own systems end-to-end in a remote-first environment.