Senior Data Engineer (Cloud Data Engineering

Accenture is a leading global provider of a broad range of professional services in strategy and consulting, interactive marketing, information technology and business operations services, with digital capabilities in all of these areas. With more than 800,000 employees worldwide, we serve clients in more than 120 countries.

Accenture globally has ranked 6th on the Great Place to Work® World’s Best Workplaces™.

Join our Cloud Data Engineering practice in Budapest and take a leading role in shaping next-generation data platforms for global enterprise clients.

As a Senior Data Engineer, you’ll architect, build, and evolve cloud-native data ecosystems that power analytics, automation, and AI workloads at scale.

This role blends hands-on engineering with technical leadership — ideal for experts who want to own delivery, guide others, and stay close to cutting-edge cloud technologies.

Scope of the position

Architect and implement cloud-native data solutions using GCP (BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Composer, Cloud Storage)

Lead the design and development of complex data pipelines and distributed processing frameworks that ensure scalability, performance, and operational reliability

Define data models, patterns, and automation frameworks for modern analytics ecosystems, including orchestration and data lifecycle governance

Translate business and analytical requirements into robust cloud architectures, collaborating closely with solution architects, data modelers, and global delivery teams

Ensure data quality, security, observability, and compliance across all stages of the data pipeline

Apply DevOps and CI/CD practices (Git, pipelines, IaC, automated testing) to ensure repeatable, high-quality delivery

Mentor and guide junior and mid-level engineers, promote best practices, and contribute to continuous improvement of the engineering community

Stay on top of emerging trends in cloud data engineering, big data processing, serverless computing, and AI/ML platform capabilities

What We Offer For You

Participation in full-cycle and diverse international projects

Opportunity to work as a specialist or manager/team lead

Constant career development with internal mentorship

Access to a whole set of learning platforms, paid certifications

Flexible working conditions, home working is allowed

Attractive base salary & Wide range of benefits included cafeteria, bonuses, private health insurance package, life insurance, AYCM sport card, referral bonus, family-oriented benefits, company shares on a discount price

Job Qualifications

Proven experience in data engineering, cloud data architecture, or advanced analytics platforms

Deep expertise with Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Dataproc, Composer) — or strong background in Azure (ADF, Synapse, Databricks) or AWS (Glue, Redshift, EMR) with readiness to transition into a GCP-focused role

Strong mastery of big data/analytics platforms such as BigQuery, Snowflake, Redshift, Synapse, or Databricks

Advanced proficiency in SQL and Python, including experience building complex transformations, orchestration workflows, and automation scripts

Strong understanding of data architecture, performance optimization, distributed processing, and cloud-native design patterns

Experience leading technical workstreams, reviewing designs, and providing engineering direction in multi-cloud or hybrid environments

Solid grasp of CI/CD pipelines, infrastructure as code (Terraform), version control, and automated deployment practices

Excellent communication and collaboration skills with distributed teams

English proficiency at B2+; German is a plus

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field

Nice to Have

Experience with BI and reporting platforms (Power BI, Tableau, Qlik, Business Objects)

Knowledge of machine learning pipelines, data science workflows, or MLOps concepts on GCP or other clouds

Understanding of containerized workloads and orchestration (Docker, Kubernetes)