Top Data Engineer Jobs in Bucharest

Maintain and expand a high-scale data platform: build and run data pipelines, implement DataOps, collaborate with analytics teams, ensure data quality and security, and design enterprise data models.

Build and deploy data products on an AWS data mesh platform, onboard data from legacy warehouses, and develop streaming and batch pipelines using Flink, SQL, Python, Kafka, Iceberg, and DynamoDB. Collaborate with product engineering teams on data contracts, ownership, documentation, and platform adoption. Provide hands-on enablement, debugging support, technical guidance, and leadership for end-to-end initiatives.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Lead and deliver end-to-end corporate data pipelines and analytical datasets, ensuring reliability, scalability, data quality, lineage, and governance. Provide hands-on technical leadership, design large-scale data processing solutions, coach data engineers, collaborate with business stakeholders across group functions, and drive enterprise data standards and AI-ready practices to support corporate reporting, KPIs, and regulatory needs.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Design, develop, and deploy Power BI solutions and data models; optimize reports, dashboards, SQL and DAX performance; manage data ingestion, ETL, batch and near-real-time processing; improve platform quality and scalability; cross-train team members; follow software engineering best practices and Agile processes to deliver high-quality data solutions for a global tax and financial automation platform.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Design and deliver analytics-ready datasets and data products for a business domain (P&C, L&H, or Finance). Translate business needs into data models, ensure data quality and governance, document lineage and metrics, and collaborate with stakeholders and platform teams to enable discoverable, self-service analytics.

Operate and automate the data plane for a sovereign Google Cloud region: manage enterprise storage (SAN/FC/iSCSI), PostgreSQL at scale, Kubernetes CSI/stateful workloads, backup/DR pipelines, and infrastructure automation. Drive durability, availability, recoverability, incident response, and capacity planning while participating in follow-the-sun on-call with Google Cloud SRE teams.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Design, build, and operate production-grade Databricks-based data pipelines across medallion architecture. Define canonical data models, ensure performance, quality, governance, and reliability. Collaborate with data scientists and stakeholders, build data integrations/APIs, mentor engineers, and continuously optimize data infrastructure and workflows.

The Senior Data Engineer will develop data solutions, build ETL processes, ensure data quality, and collaborate with the team on data analytics projects.

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Build and scale ad-tech data infrastructure to ingest, normalize, and transform massive log-level and reporting data from SSPs, DSPs, and ad servers. Design ETL/ELT pipelines, data models, and warehouses optimized for real-time analytics, implement data quality monitoring, and support BI and analytics teams while ensuring GDPR/CCPA compliance.

Maintain and scale production data ingestion pipelines processing billions of events, support and optimize complex ETL and streaming workflows, develop platform tools and ML/streaming initiatives, ensure reliability/observability, and assist data scientists and analysts with production workloads.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Design, build, and maintain scalable cloud-based data pipelines and ETL processes on Azure (Databricks, ADF, Synapse, ADLS). Implement data models, quality checks, governance, and performance optimization. Collaborate with architects, cloud engineers, data scientists, and stakeholders to consolidate legacy services and enable data-driven insights.

Design, build, and maintain scalable real-time and batch data pipelines for programmatic ad-tech. Ingest and normalize SSP/DSP/GAM data, develop ETL/ELT workflows, data models for yield and auction analytics, maintain warehouses/lakes, implement data quality monitoring, and ensure GDPR/CCPA compliance while supporting BI and analytics teams.

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

Design and implement scalable Azure data platforms, develop data pipelines, and support clients with data modernization and governance practices.