Senior Data Engineer (Banking)

Responsibilities

• Design and implementation of robust, scalable, and high-performance ETL/ELT data pipelines using PySpark/Scala and Databricks SQL on the Databricks platform;
• Strong expertise in implementing and optimizing the Medallion Architecture (Bronze, Silver, Gold) using Delta Lake, ensuring data quality, consistency, and historical tracking.
• Efficient implementation of the Lakehouse architecture on Databricks, combining best practices from traditional Data Warehousing and Data Lake paradigms;
• Optimization of Databricks clusters, Spark operations, and Delta tables (e.g. Z-Ordering, compaction, query tuning) to reduce latency and compute costs;
• Design and implementation of real-time and near-real-time data processing solutions using Spark Structured Streaming and Delta Live Tables (DLT);
• Implementation and administration of Unity Catalog for centralized data governance, fine-grained security (row- and column-level security), and end-to-end data lineage;
• Definition and implementation of data quality standards and validation rules (e.g. using DLT or Great Expectations) to ensure data integrity and reliability;
• Development and management of complex workflows using Databricks Workflows (Jobs) or external orchestration tools such as Azure Data Factory or Airflow to automate data pipelines;
• Integration of Databricks pipelines into CI/CD processes using Git, Databricks Repos, and Databricks Bundles;
• Close collaboration with Data Scientists, Analysts, and Architects to translate business requirements into optimal technical solutions;
• Providing technical mentorship to junior engineers and promoting engineering best practices across the team.

Qualifications

• Proven, expert-level experience across the full Databricks ecosystem, including Workspace management, cluster configuration, notebooks, and Databricks SQL.
• In-depth knowledge of Spark architecture (RDDs, DataFrames, Spark SQL) and advanced performance optimization techniques;
• Strong expertise in implementing and managing Delta Lake features, including ACID transactions, Time Travel, MERGE operations, OPTIMIZE, and VACUUM;
• Advanced/expert proficiency in Python (PySpark) and/or Scala (Spark);
• Expert-level SQL skills and strong experience with data modeling approaches (Dimensional Modeling, 3NF, Data Vault);
• Solid hands-on experience with a major cloud platform (AWS, Azure, or GCP), with a strong focus on cloud storage services (S3, ADLS Gen2, GCS) and networking fundamentals.


Nice to have
• Practical experience implementing and administering Unity Catalog for centralized governance and fine-grained access control;
• Hands-on experience with Delta Live Tables (DLT) and Databricks Workflows for building and orchestrating data pipelines;
• Basic understanding of MLOps concepts and hands-on experience with MLflow to support collaboration with Data Science teams;
• Experience with Terraform or equivalent Infrastructure as Code (IaC) tools;
• Databricks certifications (e.g. Databricks Certified Data Engineer Professional) are considered a significant advantage;
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field;
• 5+ years of experience in Data Engineering, including at least 3+ years working with Databricks and Apache Spark at scale.