Top Data Engineer Jobs in Bucharest

Designs and maintains analytics-ready datasets and data products for insurance and finance domains. Translates business processes into data models, metrics, and governed datasets; ensures quality, lineage, documentation, and discoverability. Uses AI-assisted analytics to accelerate exploration and decision support. Partners with business, governance, platform, analytics, and AI teams to deliver end-to-end data pipelines and enable self-service reporting and dashboards.

Design, build, and maintain scalable data pipelines, models, and backend data infrastructure. Develop dbt transformation workflows on Snowflake or Databricks, manage orchestration, and create internal data applications with tools such as Streamlit. Translate product requirements into technical solutions, collaborate across engineering and analytics teams, and use GenAI tools to accelerate development from design through deployment and continuous improvement.

Develops and supports data warehouse and ETL solutions using ODI, Oracle, Databricks, SQL, and PL/SQL. Responsibilities include data mapping, modeling, source-system analysis, ETL package development, performance tuning, testing, deployment, troubleshooting, and rollout coordination. The role collaborates with business and IT stakeholders to design technical data warehouse frameworks and deliver data-driven solutions in a hybrid work environment.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

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.

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.

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.

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.

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.

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.

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.

Develop and optimize large language models and NLP capabilities for decision support, mission planning, workflow automation, and situational awareness. Responsibilities include data preprocessing, model training and fine-tuning, RAG and agentic system development, evaluation, deployment, monitoring, documentation, and collaboration with engineering and product teams. The role requires strong Python, deep learning, LLMOps, cloud, and MLOps expertise, plus knowledge of responsible AI and bias mitigation.

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 maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.