Senior Data Engineer / Databricks

We are looking for an Experienced Data Engineer with strong hands-on experience in Databricks-based data platforms. The role focuses on building, optimizing, and maintaining scalable data pipelines, data lakes, and Lakehouse solutions that enable advanced analytics and data-driven products.

You will work on heavy data processing tasks, third-party integrations, ETL/ELT pipelines, and orchestration of data workloads in cloud environments. If you enjoy working with Databricks, Spark, large datasets, and modern cloud data stacks - and you are not constrained by a single programming language or tool - this role could be a great fit.

Key Responsibilities:

• Take ownership of data engineering features, architecture, and code quality

• Design, implement, and maintain Databricks-based data pipelines and workflows

• Build and optimize ETL/ELT processes using Apache Spark on Databricks

• Design and manage data lakes and Lakehouse architectures (Delta Lake)

• Integrate diverse data sources and ensure reliable data ingestion

• Automate orchestration, scheduling, and monitoring of Databricks jobs

• Design and implement fault-tolerant and scalable data processing workflows

• Ensure high data quality, consistency, and accuracy across the platform

• Make informed decisions about storage, compute, and performance optimization

• Collaborate with analytics, BI, and business stakeholders to support data-driven products

Required Qualifications:

• 7+ years of relevant experience as a Data Engineer

• Strong hands-on experience with Databricks and Apache Spark

• Proficiency in Python or Scala (both strongly preferred)

• Very good knowledge of SQL, relational databases, and data warehousing concepts

• Solid experience with ETL/ELT principles and data pipeline design

• Hands-on experience with cloud platforms (Azure, AWS, or GCP), preferably Databricks workloads

• Experience working with distributed systems and large-scale data processing

• Familiarity with Unix-like operating systems

• Experience with version control systems

• Strong communication skills and English language proficiency

Nice to have:

• Databricks certifications - Professional level

• Experience with Delta Lake, performance tuning, and cost optimization

• Experience with streaming technologies (Kafka or similar)

• Knowledge of workflow orchestration tools (Databricks Workflows, Airflow, etc.)

• Experience with cloud-native and serverless data architectures

• Familiarity with containerization and virtualization (Docker, Kubernetes)

• Experience building data assets that directly support analytics and business decision-making