Top Data Engineer Jobs in Bucharest

Develops and supports data warehouse and ETL solutions using ODI, Oracle, Databricks, SQL, and PL/SQL. Responsibilities include data modeling, source system analysis, data mapping, ETL package development, performance tuning, testing, deployment, rollout coordination, troubleshooting, and maintenance. The role collaborates with business and IT stakeholders to design data flows and technical frameworks, while leveraging data profiling, cataloging, visualization, and large-scale database analysis.

Design and deliver Azure-based data platforms and pipelines using Databricks, Medallion Architecture, Delta Lake, and dbt. Analyze source systems, data quality, governance, integration requirements, and KPI feasibility across HR, payroll, finance, and operational data. Produce technical designs, standards, estimates, and risk assessments while collaborating with architects, engineers, clients, and business stakeholders through workshops and architecture reviews.

Build and maintain ETL/ELT pipelines, cloud data warehouse models, dashboards, and reports. Ensure data quality, governance, lineage, and reliable metric definitions while translating stakeholder needs into actionable analytics. Support AI-enabled reporting, natural-language querying, anomaly detection, and governed LLM tools. Collaborate with business, IT, and engineering teams to automate reporting and improve self-service data access.

Designs, builds, and maintains scalable batch and streaming data pipelines on GCP using BigQuery, dbt, Kafka, SQL, and Python. Responsibilities include data modeling, data quality, legacy-system migration, monitoring and observability, cost and performance optimization, governance, and enabling self-service data products. The role collaborates with architects, platform engineers, analytics teams, and clients in complex international environments.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

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 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.

Design and implement scalable Databricks Lakehouse architectures while migrating an enterprise analytics platform from BigQuery. Develop and optimize PySpark and SQL pipelines, Delta Lake medallion layers, incremental processing frameworks, and reusable platform components. Apply Unity Catalog governance, monitor and optimize Spark workloads, support CI/CD and production deployments, and collaborate with international data, analytics, product, and engineering teams on architecture and modernization initiatives.

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.

Own and modernize Xeneta’s end-to-end data architecture, including Snowflake and dbt models, data pipelines, governance, metadata, lineage, and quality automation. Improve data discoverability and reliability for engineers, analysts, data scientists, and AI agents. Establish scalable standards, automate access and onboarding processes, and educate teams on AI-enabled data workflows.

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.

Design, implement, and manage scalable cloud-native batch and real-time data pipelines. Process structured and unstructured data, optimize ETL/ELT and data models, evaluate new tools, collaborate with data scientists and analysts, automate Unix-based operations, and document best practices.

Design, build, and maintain scalable Databricks data pipelines using Python and SQL. Develop ETL/ELT workflows, optimize cloud data solutions for performance and cost, maintain data models and integrations, and provide reliable data for analytics and AI initiatives. Monitor and troubleshoot pipelines, document best practices, collaborate with analysts and scientists, and contribute to modern lakehouse, MLOps, and generative AI projects.

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.

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.

Build and support production-grade data pipelines using PySpark, SQL, Databricks, and GCP. Re-engineer ingestion processes, implement data mappings, lineage, reconciliation, hierarchy and roll-up logic, and establish automated data-quality, testing, monitoring, and deployment controls. Collaborate with engineers, analysts, business SMEs, and platform teams to deliver governed reporting capabilities, investigate data variances, manage controlled transitions, and produce technical documentation and handover materials.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Build and maintain high-performance data lakehouse infrastructure using Java and Rust. Develop reliable Apache Iceberg maintenance workflows, including compaction, snapshot expiry, manifest rewriting, and orphan-file cleanup. Ensure correctness under concurrency, retries, and failures. Maintain a Rust-based SQL query router, contribute connectors and open-source reviews, support production systems, and participate in on-call debugging and roadmap development.

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.

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 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.

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.

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.