Job Description

Roles & Responsibilities

• · Design, develop, and maintain robust, scalable data pipelines using Azure Data Factory (ADF) and Databricks · Implement batch and real-time data ingestion frameworks leveraging Confluent Kafka · Build and optimize data models, transformations, and ETL/ELT pipelines using SQL and Spark · Develop and manage data storage solutions in Azure Data Lake Storage (ADLS) · Ensure high data quality, integrity, and governance standards · Support data integration across systems including core banking and CRM · Optimize performance of large-scale datasets · Collaborate with stakeholders to translate requirements into solutions · Ensure compliance with banking regulations and data privacy · Troubleshoot production issues

Desired Candidate Profile

• Required Skills & Experience · Strong expertise in SQL (complex queries, tuning, modeling) · Hands-on experience with Databricks, Spark, PySpark · Experience with Confluent Kafka and event-driven architecture · Experience with Azure Data Factory (ADF) · Experience with Azure Data Lake Storage (ADLS) · Understanding of data warehousing concepts · Familiarity with CI/CD and DevOps for data platforms Domain Knowledge (Preferred) · Experience in banking or financial services · Understanding of core banking systems · Knowledge of payments, lending, wealth management · Awareness of regulatory frameworks (KYC, AML) Soft Skills · Strong analytical and problem-solving skills · Excellent communication and stakeholder engagement · Ability to work in fast-paced environments · Strong ownership and collaboration mindset Qualifications · Bachelor’s or Master’s degree in relevant field · Azure/Data engineering certifications preferred Good to Have · Experience with Python or Scala · Exposure to data governance tools · Knowledge of APIs and microservices · Experience with BI tools like Power BI