Data Engineer

• Develop and implement machine learning models using Python to drive business insights.

• Manage large datasets on Hadoop, Snowflake, and Teradata, ensuring efficient storage and processing.

• Utilize AWS (S3, Glue, Lambda, EC2, etc.) and Google Cloud (BigQuery, Cloud Storage, Dataflow, etc.) for data integration, processing, and cloud-based analytics.

• Work with FSDM (Financial Services Data Model) to design and optimize financial data models for structured reporting and analysis.

• Create dashboards and reports using Qlik to support data-driven decision-making.

• Develop ETL workflows to ingest and transform structured and unstructured data.

• Collaborate with business stakeholders to translate complex data into actionable insights.

• Ensure data security, governance, and compliance across cloud and on-premise environments.

Required Skills & Experience

• Python – Strong proficiency in data processing, automation, and machine learning.

• Hadoop, Snowflake, Teradata (FS-LDM) – Experience with big data platforms and financial data modeling.

• AWS (S3, Glue, Redshift, Lambda) & Google Cloud (BigQuery, Cloud Storage, Dataflow, etc.) – Hands-on expertise in cloud-based data engineering.

• Qlik – Ability to develop interactive dashboards and reports.

• SQL & ETL – Strong background in data extraction, transformation, and integration.

• Teradata Financial Services Data Model (FSDM) – Familiarity with its application.