Job
Description
The ideal candidate should possess extensive expertise in SQL, data modeling, ETL/ELT pipeline development, and cloud-based data platforms like Databricks or Snowflake. You will be responsible for designing scalable data models, managing reliable data workflows, and ensuring the integrity and performance of critical financial datasets. Collaboration with engineering, analytics, product, and compliance teams is a key aspect of this role. Responsibilities: - Design, implement, and maintain logical and physical data models for transactional, analytical, and reporting systems. - Develop and oversee scalable ETL/ELT pipelines to process large volumes of financial transaction data. - Optimize SQL queries, stored procedures, and data transformations for enhanced performance. - Create and manage data orchestration workflows using tools like Airflow, Dagster, or Luigi. - Architect data lakes and warehouses utilizing platforms such as Databricks, Snowflake, BigQuery, or Redshift. - Ensure adherence to data governance, security, and compliance standards (e.g., PCI-DSS, GDPR). - Work closely with data engineers, analysts, and business stakeholders to comprehend data requirements and deliver solutions. - Conduct data profiling, validation, and quality assurance to maintain clean and consistent data. - Maintain comprehensive documentation for data models, pipelines, and architecture. Required Skills & Qualifications: - Proficiency in advanced SQL, including query tuning, indexing, and performance optimization. - Experience in developing ETL/ELT workflows with tools like Spark, dbt, Talend, or Informatica. - Familiarity with data orchestration frameworks such as Airflow, Dagster, Luigi, etc. - Hands-on experience with cloud-based data platforms like Databricks, Snowflake, or similar technologies. - Deep understanding of data warehousing principles like star/snowflake schema, slowly changing dimensions, etc. - Knowledge of cloud services (AWS, GCP, or Azure) and data security best practices. - Strong analytical and problem-solving skills in high-scale environments. Preferred Qualifications: - Exposure to real-time data pipelines like Kafka, Spark Streaming. - Knowledge of data mesh or data fabric architecture paradigms. - Certifications in Snowflake, Databricks, or relevant cloud platforms. - Familiarity with Python or Scala for data engineering tasks.,
