Top Data Engineer Jobs in Romania

Build and maintain cloud-based media data pipelines and user-facing data products. Design, deploy and govern BigQuery/DBT pipelines, APIs and dashboards, ensure data quality/security, manage vendors, and drive product-oriented delivery and adoption.

Build and migrate large-scale data platforms to Snowflake, adapting approximately 1,000 datasets and existing pipelines. Develop, test, schedule, and monitor reliable Apache Airflow workflows using Python, Spark, Kafka, Iceberg, and AWS S3. Support Airflow, Confluent, and data-format migrations while ensuring data quality, performance, and reliability. Collaborate with data platform teams on modernization initiatives and time-sensitive migrations.

Execute large-scale data platform migrations to Snowflake, adapting pipelines and migrating approximately 1,000 datasets. Develop, test, monitor, and troubleshoot Airflow workflows using Python and Spark. Support migrations involving Airflow 2 to 3, Confluent regions, and Iceberg while maintaining data quality, reliability, and performance. Collaborate with data platform teams on modernization initiatives and meet time-sensitive delivery targets.

Build and migrate large-scale data platforms to Snowflake, adapting approximately 1,000 datasets and existing pipelines. Develop, test, monitor, and troubleshoot Airflow workflows using Python, Spark, Kafka, Iceberg, Parquet, and AWS S3. Ensure data quality and reliability while supporting Snowflake, Airflow, Confluent, and Iceberg modernization initiatives.

Build and maintain ETL pipelines supporting an SAP ECC6-to-S/4HANA migration. Responsibilities include data extraction, transformation, loading, quality validation, reconciliation, rejection handling, documentation, and continuous delivery improvement. The role collaborates closely with technical and functional stakeholders within an agile data migration squad and works with large, complex datasets.

Designs and optimizes scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Builds agentic workflows to automate validation, troubleshooting, and operational processes; develops reusable frameworks, implements data quality and monitoring controls, optimizes workloads, and supports testing, deployment, productionization, and ongoing maintenance. Collaborates with business, data engineering, and platform teams to identify automation opportunities.

Build and maintain scalable batch and near-real-time ETL pipelines for Deutsche Bank’s cloud data platform. Develop maintainable Python code, transform and model data, integrate Google Cloud services, troubleshoot pipeline issues, and contribute to code reviews, CI/CD, testing, and agile collaboration. The role focuses on delivering reliable data for reporting, analytics, and operational use within the Private Bank.

Designs, builds, and operates scalable batch and near-real-time data pipelines using Microsoft Fabric. Develops Lakehouse and Warehouse solutions, integrates diverse data sources, applies data modeling and transformation techniques, and optimizes performance and reliability. Implements data quality, security, observability, testing, CI/CD, and production support practices while collaborating with technical and business stakeholders. Documents standards and evaluates new Fabric capabilities.

Designs and maintains scalable GCP cloud platforms for migration, self-service, automation, and AI enablement. Responsibilities include Terraform-based infrastructure as code, CI/CD pipelines, cloud-native architecture, observability, governance, security, cost optimization, and platform reliability. The role collaborates with data engineers, architects, and delivery teams while developing reusable engineering patterns and supporting large-scale enterprise cloud transformation.

Design and deliver Azure-based data platforms and pipelines using Databricks, Medallion Architecture, Delta Lake, and dbt. Analyze source systems, data quality, governance, integration requirements, and KPI feasibility across HR, payroll, finance, and operational data. Produce technical designs, standards, estimates, and risk assessments while collaborating with architects, engineers, clients, and business stakeholders through workshops and architecture reviews.

Build and maintain ETL/ELT pipelines, cloud data warehouse models, dashboards, and reports. Ensure data quality, governance, lineage, and reliable metric definitions while translating stakeholder needs into actionable analytics. Support AI-enabled reporting, natural-language querying, anomaly detection, and governed LLM tools. Collaborate with business, IT, and engineering teams to automate reporting and improve self-service data access.

Designs, builds, and maintains scalable batch and streaming data pipelines on GCP using BigQuery, dbt, Kafka, SQL, and Python. Responsibilities include data modeling, data quality, legacy-system migration, monitoring and observability, cost and performance optimization, governance, and enabling self-service data products. The role collaborates with architects, platform engineers, analytics teams, and clients in complex international environments.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

Designs and maintains Matillion-on-AWS ETL/ELT pipelines and Snowflake data infrastructure across raw, silver, and gold layers. Builds certified Data Products for an enterprise Data Marketplace, implementing governance, data contracts, quality checks, lineage, metadata, monitoring, and access controls. Collaborates with analysts, data scientists, AI/ML teams, product owners, and business stakeholders. Develops reusable frameworks, CI/CD automation, certification workflows, and performance optimizations while contributing to architecture and mentoring engineers.

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.

Implement and improve the data centre health and safety management system. Conduct risk assessments, RAMS reviews, inspections, audits, incident investigations, and contractor safety activities. Deliver training and toolbox talks, monitor safety KPIs, maintain emergency response plans, and ensure compliance with legislation, standards, and company policies. The role requires regular site presence and occasional travel between sites.

Designs and maintains data marts, ETL/ELT pipelines, dimensional data models, and recurring reporting datasets. Transforms raw data using SQL and Python, orchestrates production workflows with Azure Data Factory and Databricks, and implements data quality checks and reconciliations. Monitors pipeline health, troubleshoots failures, documents data processes, and collaborates with analysts, data scientists, business stakeholders, architecture, and DevOps teams. The role is fully remote but restricted to candidates permanently located in Romania.

Develop and maintain Snowflake data pipelines and ETL/ELT processes; write advanced SQL, stored procedures, views, and transformations; implement incremental loading with Snowpipe, Streams, and Tasks; design data models; validate and reconcile data; optimize performance; troubleshoot production issues; and apply role-based security controls.

Design and build cloud-native data platforms, lakehouse architectures, ETL/ELT pipelines, streaming systems, and data services on AWS. Develop data warehouse and real-time pipeline solutions, support AI features such as RAG and vector search, and contribute to modernization projects. Collaborate with customers and internal teams on technical designs, troubleshooting, code reviews, performance tuning, governance, monitoring, and production support.

Establishes and operates a governed, enterprise-scale Databricks and Snowflake platform. Responsibilities include designing onboarding, deployment, governance, monitoring, cost transparency, self-service automation, infrastructure-as-code, and DevOps tooling. The role supports internal data and analytics customers, collaborates with architecture, security, and governance teams, ensures compliance, develops documentation and training, and aligns platform capabilities with advanced analytics and AI/ML needs.

Design and operate scalable batch and real-time data pipelines using Python, PySpark, SQL, BigQuery, and Google Cloud services. Build reliable data models, optimize performance, enforce data quality, and support reporting and analytics. Collaborate with engineering, analytics, and business teams while contributing to CI/CD, testing, code reviews, and cloud-native platform modernization. The role also involves troubleshooting complex pipelines and responsibly using AI tools to improve productivity and solve business problems.

Designs, builds, and maintains scalable Databricks data pipelines, ETL/ELT workflows, and cloud-based data lake and warehouse platforms. Develops orchestration, data quality, governance, lineage, and monitoring frameworks. Partners with stakeholders, BI teams, and engineers to create data models and analytics-ready datasets. The role requires strong cloud, data engineering, governance, communication, and problem-solving skills.

Owns production data integrations for an AI-assisted KYC and customer due diligence platform. Responsibilities include mapping data sources, defining data contracts and canonical models, building connectors, integrating Databricks with SQL Server and legacy systems, supporting APIs and SFTP workflows, implementing data-quality checks and contract tests, and validating reliable production data flows.

Build scalable customer data marts, real-time event-driven pipelines, and backend APIs for an AI-powered personalization product. Model and transform data using BigQuery and Dataform, orchestrate workflows across GCP services, integrate customer data and engagement platforms, and manage infrastructure with Terraform and CI/CD through GitHub Actions. The role requires expert Python and SQL skills, strong GCP experience, data modeling expertise, and fluent English.

Designs and operates production machine learning systems for predictive customer lifetime value. Responsibilities include feature engineering, gradient-boosted and probabilistic modeling, calibration, identity resolution, model monitoring, drift detection, data pipelines, cloud-based ML workflows, API integrations, and software engineering practices such as testing, CI/CD, and deployment safeguards.