Top Data Engineer Jobs in Romania

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Design, build, test, deploy, and operate scalable Azure and Databricks data pipelines and analytical data products. Refactor pipelines for reliability, performance, observability, and maintainability; diagnose incidents; and implement SRE, DataOps, CI/CD, testing, monitoring, governance, and security practices. Collaborate with data, architecture, product, and engineering teams to onboard workloads, translate reporting needs into technical solutions, document data flows, and share engineering practices.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Build and maintain scalable data pipelines, warehouses, lakes, and cloud data platforms supporting analytics and business intelligence. Develop ETL/ELT workflows, optimize data models and databases, ensure data quality, security, governance, and reliability, and collaborate with IT, engineering, analysts, scientists, and business stakeholders. Contribute to dashboards, reports, data products, technical documentation, and data management best practices.

Design and maintain scalable data pipelines and platforms for industrial and operational data. Integrate legacy systems, IoT, time-series data, and technical documentation while implementing cloud data architectures, governance, metadata, quality, and lineage practices. Prepare curated datasets for analytics, predictive maintenance, digital twins, machine learning, and AI use cases. Support Power BI dashboards and collaborate with data scientists, engineers, and domain experts.

Design and optimize scalable Python data ingestion and document-processing pipelines for unstructured insurance data. Integrate OCR, document extraction, cloud services, enterprise repositories, vector databases, and RAG workflows. Build parsing, normalization, chunking, metadata, monitoring, and quality-control processes while meeting security, compliance, performance, and availability requirements. Collaborate with AI, backend, and platform teams to deliver cloud-native AI document solutions.

Develop and maintain data pipelines for gas sales and compliance platforms. Responsibilities include ETL and data transformations in Azure Synapse, ingestion from structured and unstructured sources, pipeline orchestration with Azure Synapse and Data Factory, maintaining data product documentation, and supporting incidents requiring code fixes or workarounds.

Lead Salesforce data migrations by defining mapping, transformation, loading, reconciliation, and validation strategies. Extract, cleanse, deduplicate, and validate data; create source-to-target mappings; execute migrations using Data Loader, ETL tools, or APIs; perform testing and data quality checks; and support integrations with ERP, marketing, and data platforms.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Design and develop an AI-powered analytics agent in Devin, integrating GitHub, dbt, Databricks, Snowflake, and data ingestion systems. Adapt dbt workloads for Databricks, resolve platform compatibility issues, build and test agent capabilities, and implement guardrails, validation, observability, security, and reliable AI-assisted workflows. Collaborate with client engineering teams, troubleshoot integrations, document the solution, and prepare knowledge-transfer materials.

Build and improve scalable data platforms, backend services, and data pipelines. Design data transformations, apply SQL-based modeling, troubleshoot complex systems, collaborate with stakeholders, contribute to architecture, and maintain code quality through testing, reviews, and documentation. The role requires strong software engineering fundamentals and analytical problem-solving, with opportunities to develop expertise in data engineering, analytics, cloud platforms, and data-intensive systems.

The Senior Product Data Engineer will build, iterate, and launch data products, focusing on large-scale data processing and ETL/ELT pipelines, using various technologies and implementing AI features.

Lead and coach a data engineering team while building, testing, maintaining, and documenting data pipelines and APIs. Ensure reliable transactional and analytical data delivery through testing, controls, and engineering best practices. Collaborate with business, IT, and data stakeholders in agile ceremonies and design sessions. Apply Python, PySpark, SQL, CI/CD, Git workflows, distributed programming, data modeling, slowly changing dimensions, and change data capture techniques.

Designs, builds, and operates reliable ETL/ELT pipelines and analytics-ready datasets using Python, Azure Databricks, and SQL. Responsibilities include orchestration, monitoring, production support, performance tuning across SQL Server and PostgreSQL, data quality controls, and operational runbooks. The role also uses generative AI and integrates AI capabilities into enterprise data workflows while applying responsible AI principles such as privacy, fairness, and explainability.

Lead a data engineering team developing, testing, documenting, and maintaining data pipelines, services, and data artefacts. Ensure reliable transactional and analytical data delivery through testing, controls, and engineering best practices. Collaborate with IT, data, business, and international stakeholders through Agile ceremonies and design workshops. Support implementation of centralized standards and coordinate local delivery while contributing to APIs, CI/CD practices, and scalable distributed data processing.

Design, build, and optimize scalable Databricks data pipelines using PySpark, Python, SQL, and Delta Lake. Develop ETL/ELT processes, data quality controls, monitoring, and observability solutions. Integrate data platforms with Java and Spring Boot applications through REST APIs and microservices. Create reusable frameworks, support CI/CD, troubleshoot production issues, and contribute to architecture, modernization, and technical roadmap decisions while applying AI tools to improve delivery.

Lead a data engineering team developing, testing, maintaining, and documenting data pipelines and API services for transactional and analytical needs. Apply data engineering best practices, testing controls, distributed programming, dimensional modeling, slowly changing dimensions, and change data capture. Collaborate across agile squads, coordinate local adoption of central standards, and communicate with business, data, and IT stakeholders.

Designs and maintains analytics-ready datasets and data products for dashboards, reporting, AI, and decision-making across insurance or finance domains. Translates business processes into data models and reusable metrics, ensures quality, governance, lineage, and documentation, and uses AI-assisted analytics. Partners with business, platform, governance, analytics, and AI teams to deliver scalable, discoverable datasets from ingestion through consumption.

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.