Senior Data Engineer / Data Analyst

Senior Data Engineer / Data Analyst

Requirements

Must have:

• Responsible for the management, transformation, optimization, and accessibility of financial and banking data within enterprise data environments. The role focuses on processing large volumes of client, investor, account, transaction, movement, office, contact, asset, and liability information, ensuring that raw data is properly filtered, cleansed, structured, and made available for downstream systems and reporting.
• The work involves maintaining and improving data flows across Golden Source, Oracle, Snowflake, AWS, and reporting environments, while supporting daily processes and continuously changing data.
• Strong experience with Oracle databases and SQL.
• Experience working with large-scale financial and client datasets.
• Strong understanding of data transformation, ETL/ELT processes, and data warehousing concepts.
• Experience working with Snowflake and cloud-based data environments.
• Experience with AWS and cloud-based data sources.

Nice to have:

• Experience working with financial and banking data, including transactions, accounts, client and investor information, assets, liabilities, and related records.
• Experience working with large-scale datasets containing millions of records.
• Experience with Snowflake data warehouse architecture and optimization.
• Familiarity with dimensional data models and connections between data warehouse dimensions.
• Experience supporting daily batch processes and frequently changing data.
• Familiarity with metadata management, data lineage, and data flow optimization.
• Experience working with enterprise data architectures involving multiple source systems, databases, cloud environments, integration platforms, and reporting systems.

Responsibilities

• Process and transform raw financial and banking data into structured, cleansed, and optimized datasets for easier access and querying.
• Develop and maintain data processes within Oracle databases, working across different schemas according to business and data requirements.
• Create and optimize SQL queries used to extract, filter, validate, transform, and organize financial and client data.
• Maintain data flows between Golden Source, Oracle, Snowflake, and Reporter environments.
• Build and maintain connections between the data warehouse and the different dimensions within the data architecture.
• Work with large-scale datasets containing millions of records related to clients, investors, accounts, transactions, movements, contacts, offices, assets, liabilities, and account information.
• * Process data received from client-provided sources and AWS cloud environments.
• * Work with different data formats, including database records, CSV files used in daily processes, and JSON files used for data comparison and storage.
• * Maintain daily data processes and support the management of constantly changing or hot data.
• * Monitor data processes and investigate data issues to ensure that information is correctly processed and delivered to downstream systems.
• * Identify and resolve data inconsistencies, transformation issues, and problems occurring throughout the data flow.
• * Support and maintain data integration processes using IICS.
• * Schedule and monitor recurring data processes using Autosys.
• * Use Python for data processing, automation, validation, and supporting data engineering tasks.
• * Use GitHub for source control, version management, and collaboration on data engineering projects.
• * Ensure data quality, consistency, integrity, and accessibility throughout the different stages of the data architecture.

Required Languages

English Developing Advanced

Location

Aguascalientes, México, Gdl, Mty., Ens.