Top Data Engineer Jobs in Romania

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.

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, 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.

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.

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.

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.

Lead the architecture and development of a multi-country Snowflake and GCP data platform. Build CDC ingestion pipelines, dbt semantic models, and DataOps CI/CD workflows; implement governance, RBAC, PII masking, compliance, FinOps, and performance optimization. Partner with business stakeholders, document architectural decisions, and mentor data engineers across LATAM operations.

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 and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Develop and deploy scalable, production-grade machine learning and AI solutions for financial research. Build LLM- and NLP-based applications, collaborate with senior engineers, and contribute to reliable AI-driven products. Responsibilities require Python, software engineering, deep learning, databases, Docker, and at least one major deep learning framework. Preferred experience includes RAG pipelines, vector databases, LLM fine-tuning, cloud platforms, and Kaggle competitions.

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.

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 the architecture and development of scalable Databricks data solutions for global clients. Build PySpark pipelines, migrate legacy warehouses to Delta Lake, optimize Spark performance and costs, implement governance with Unity Catalog, and establish code quality and CI/CD standards. Advise stakeholders on data strategy, mentor engineers, and deliver modern Lakehouse architectures using cloud platforms and contemporary orchestration tools.

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.

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 design, build and maintain scalable data pipelines using Azure Data Factory, Databricks and SQL Server. Develop Python (PySpark) automation and data processing, optimize data models and queries, resolve pipeline issues, improve data quality and resilience, collaborate with cross-functional teams, and mentor junior engineers while establishing engineering and governance best practices.

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.

Build and operate resilient, large-scale backend and data collection systems managing 400M+ creator profiles and billions of data points. Design distributed services that handle API instability, rate limits, outages, and partial failures; optimize collection economics; and own architecture, implementation, deployment, observability, and production operations. Collaborate with data and product teams to improve freshness, reliability, coverage, and system performance.