Lead Data Engineer

Job - Lead Data Engineer

Location: - Any

Exp. - 10+ Yrs

Mode: - Remote

Job Description: -

Role Objective:
We are seeking a seasoned Data Professional to drive the design and implementation of a modern Data Lakehouse for a major financial services program. The ideal candidate will be an expert in the Teradata FSLDM framework and possess deep expertise in Informatica for complex ETL/ELT orchestration. You will be responsible for transforming raw financial data into a structured, high-performance Lakehouse architecture that supports both BI and advanced analytics.

Key Responsibilities:
Data Modelling: Lead the implementation and customization of the Teradata FSLDM (Financial Services Logical Data Model) to ensure it meets the specific needs of the Lakehouse program.

Architecture Design: Design and maintain the Data Lakehouse layers (Bronze/Silver/Gold or Raw/Integrated/Access) to support massive scales of financial data.

ETL/ELT Development: Architect and develop robust data pipelines using Informatica (PowerCenter or IICS) to migrate data from disparate sources into Teradata and the Lakehouse environment.

Performance Tuning: Optimize Teradata SQL and Informatica mappings for high-volume data processing and complex financial calculations.

Data Governance: Ensure compliance with financial regulations by implementing data lineage, quality checks, and metadata management within the FSLDM framework.

Stakeholder Collaboration: Work closely with Business Analysts and Data Scientists to translate financial business requirements into scalable technical schemas.

Technical Requirements:
Core Model: Expert-level knowledge of FSLDM (Financial Services Logical Data Model) is mandatory.

Primary Database: Extensive experience with Teradata (Vantage, Architecture, Utilities like BTEQ, FastLoad, MultiLoad).

Integration Tools: Advanced proficiency in Informatica (PowerCenter/Informatica Intelligent Cloud Services).

Lakehouse Experience: Proven experience in building or maintaining Data Lakehouse architectures (combining the flexibility of data lakes with the performance of data warehouses).

Domain Knowledge: Strong understanding of Banking/Financial Services domains (Risk, Finance, Regulatory Reporting, or Retail Banking).