Top Data Engineer Jobs in Bucharest

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Supports the development, testing, monitoring, and maintenance of data pipelines. Performs ETL across databases, files, APIs, and cloud platforms; validates data quality; resolves basic processing failures; writes SQL and transformation scripts; documents pipelines and procedures; follows security and governance standards; supports releases, code reviews, and continuous improvement under senior engineer guidance.

Engineer and manage Python-based data ingestion workflows, integrating pipelines with ServiceNow and downstream systems. Use Snowflake for data storage and optimization, OpenTelemetry for observability, and SQL for validation and manipulation. Troubleshoot pipeline failures, maintain data integrity, and improve workflow reliability. Preferred experience includes orchestration tools, ITIL practices, and CI/CD pipelines for data products.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Develops and supports data warehouse and ETL solutions, including ODI packages, Oracle procedures, and Databricks jobs. Responsibilities include data mapping, source-system analysis, data modeling, testing, optimization, deployment, troubleshooting, database rollout coordination, and technical framework design. The role collaborates with business and IT stakeholders and requires advanced SQL and PL/SQL skills, large-scale database experience, performance tuning, and familiarity with data visualization tools. Python, PySpark, banking experience, and Agile knowledge are advantageous.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Maintain SQL Server data platforms, ETL solutions, data models, and warehouse structures; integrate multiple data sources; optimize data quality, scalability, and performance; and support Power BI analytics. Collaborate with BI teams and contribute to cloud data architecture implementations under senior guidance.

Design and implement cloud-native data platforms and lakehouse architectures on AWS. Build ETL/ELT pipelines, streaming systems, data services, and AI solutions including RAG and vector search. Apply data modeling, governance, orchestration, observability, and reliability practices. Collaborate with customers and internal teams on technical designs, modernization projects, troubleshooting, code reviews, performance tuning, and production support.

Develop and support Qlik Sense data visualization applications, Qlik and SAP HANA data models, and data pipelines. Deliver solutions through sprint planning, manage application incidents and requests, and produce user and development documentation. Collaborate with data transport and SAP BI teams while supporting internal users in a multicultural environment.

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, 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 and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

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.