Data Engineer
Summary
Data Engineer focused on building data pipelines for the banking sector using Databricks and Apache Spark. The role involves managing streaming data with Kafka, handling CDC from RDBMS like SQL Server or Oracle, and working within Azure or AWS cloud environments to maintain Lakehouse architectures.
Data Bricks Engineer Banking
58 years of experience designing and building data pipelines using Apache Spark Databricks or equivalent bigdata frameworks.
Hands on expertise with streaming and messaging systems such as
Apache Kafka (publish subscribe architecture) Confluent Cloud
RabbitMQ or Azure Event Hub. Experience creating producers
consumers and topics and integrating them into downstream
processing.
Deep understanding of relational databases and CDC. Proficiency
in SQL Server Oracle or other RDBMSs; experience capturing
change events using Debezium or native CDC tools and
transforming them for downstream consumption.
Proficiency in programming languages such as Python Scala or
Java and solid knowledge of SQL for data manipulation and
transformation.
Cloud platform expertise. Experience with Azure or AWS services for
data storage compute and orchestration (e.g. ADLS S3 Azure
Data Factory AWS Glue Airflow DBX DLT).
Data modelling and warehousing. Knowledge of data Lakehouse
architectures Delta Lake partitioning strategies and performance
optimisation.
Version control and DevOps. Familiarity with Git and CI/CD
pipelines; ability to automate deployment and manage
infrastructure as code.
Strong problem solving and communication skills. Ability to work
with cross functional teams and articulate complex technical
concepts to nontechnical stakeholders.
Preferred/ Bonus Skills
- Experience with event driven architectures and micro services
integration. - Exposure to NiFi Flume or other ingestion frameworks for
connecting heterogeneous sources. - Knowledge of graph processing or machine learning pipelines on
Spark.
Required Experience:
IC