What you bring:
- 5+ years of experience in Data Engineering.
- Strong hands-on experience with Databricks and Apache Spark.
- Advanced knowledge of PySpark, Python, and SQL.
- Proven experience designing and implementing production-grade ETL/ELT solutions.
- Experience building modern data platforms and Lakehouse architectures.
- Strong understanding of Delta Lake, data modelling, and distributed data processing.
- Experience with batch and streaming data pipelines.
- Strong knowledge of Spark optimization and performance tuning.
- Experience working with Azure, AWS, or GCP.
- Familiarity with Git, CI/CD, testing, and modern software engineering practices.
- Strong communication and stakeholder management skills.
- Ability to lead technical initiatives and drive projects independently from design through production.
- Experience mentoring or guiding other engineers is highly valued.
- Excellent English language skills and native Bulgarian are a must
- Nice to have
- Unity Catalog
- Databricks Asset Bundles
- Lakeflow
- Auto Loader
- Structured Streaming
- Terraform
- dbt
- MLflow
- Databricks Data Engineer Professional Certification
Shape the future of Data & AI with Deloitte
At Deloitte, we help leading organizations transform the way they use data, analytics, and AI to solve complex business challenges. As a Senior Databricks Data Engineer, you will play a key role in designing and delivering modern cloud-based data platforms that power business-critical insights, advanced analytics, and AI-driven solutions.
This is more than a traditional Data Engineering role. You will own end-to-end solutions, influence architectural decisions, work directly with clients and stakeholders, and help shape engineering best practices across our growing Data & AI practice.
Working alongside Data Architects, Data Scientists, AI specialists, and business leaders, you will build scalable Lakehouse solutions using the latest Databricks technologies while contributing to high-impact transformation projects across industries and international markets.
What we offer:
- Why Deloitte
- Work on Data & AI transformation
- Build modern data platforms that power analytics, machine learning, and AI.
- Take ownership
- Lead solutions end-to-end, from architecture and design to deployment and production.
- Learn from the best
- Collaborate with experienced Architects, Data Engineers, Data Scientists, and AI specialists across Deloitte's global network.
- Accelerate your growth
- Benefit from continuous learning, certifications, mentorship, and clear career progression.
- Work across industries
- Deliver impactful solutions for clients in Financial Services, Retail, Energy, Manufacturing, Telecom, and the Public Sector.
- Join a high-performing team
- Be part of a collaborative culture focused on innovation, technical excellence, and knowledge sharing.
,[Lead the design and delivery of end-to-end Data Engineering solutions on the Databricks platform., Take ownership of projects from requirements gathering and solution design through deployment and production support., Design and build scalable data pipelines using Databricks, PySpark, Python, and SQL., Develop modern Lakehouse architectures using Delta Lake and Medallion Architecture principles., Deliver batch, incremental, and streaming data processing solutions., Partner with Data Architects, Data Scientists, and business stakeholders to turn complex requirements into scalable technical solutions., Define engineering standards, reusable frameworks, and development best practices., Optimize Spark workloads for performance, scalability, reliability, and cost efficiency., Implement data governance and security controls using Unity Catalog., Lead code reviews, technical workshops, and architecture discussions., Drive CI/CD, automation, testing, and deployment best practices., Troubleshoot and resolve complex production and performance challenges., Mentor and support Data Engineers, contributing to a culture of technical excellence and continuous learning.] Requirements: AI, Databricks, Data engineering, Spark, PySpark, Python, SQL, ETL, Data modelling, Data pipelines, Performance tuning, Azure, AWS, GCP, Git, Testing, Stakeholder management, UNITY, Terraform, dbt, MLflow