Description: -Data Engineer
Position Summary: Major Financial Firm s seeking an Data Engineer to play a key role in modernizing the firm's data platform and advancing its data and analytics capabilities. This is a transformational role directly aligned with Real Estate's commitment to leveraging data, analytics, and technology to proactively address business challenges and unlock new opportunities.
The Data Engineer will be responsible for supporting and enhancing existing ETL processes for the on- prem data lake (PostgreSQL) while leading the evolution and migration of these processes to Microsoft Fabric. This role will focus heavily on developing and maintaining SQL-based transformation logic to ensure high quality, trusted, and analytics ready data.
Working closely with the Data Management team, the Data Engineer will help identify, remediate, and prevent data quality issues by encoding validation rules and controls directly into data pipelines. The role will also contribute to the evaluation and implementation of tools and frameworks supporting data governance, architecture, and data quality as the platform continues to evolve.
Duties & Responsibilities: - Support and enhance existing ETL pipelines for an on-premises data lake environment, primarily backed by PostgreSQL.
- Refactor and migrate data ingestion, transformation, and orchestration processes to Microsoft Fabric using modern cloud-native patterns.
- Design, develop, and maintain complex SQL-based transformation logic, including views, functions, and analytical datasets.
- Build and optimize data models to support analytics and reporting use cases, including Power BI semantic models.
- Perform aggregations and transformations across multiple data models (e.g., normalized, dimensional, lakehouse) to enable reliable insights.
- Partner closely with data analytics and reporting teams to understand consumption needs and ensure data is structured for performance, usability, and scalability.
- Implement and enforce data quality rules, validation checks, and automated error detection within ETL and transformation workflows.
- Support master and reference data processes, including defining data quality criteria, uniqueness rules, and remediation approaches.
- Collaborate with domain owners and data stewards to analyze root causes of data quality issues and drive corrective actions.
- Monitor cross-domain data provisioning to ensure data is sourced from approved and governed systems.
- Contribute to the evaluation and implementation of data governance, architecture, and quality tools in partnership with Global Technology teams.
- Document data pipelines, transformation logic, and architectural decisions to support maintainability and knowledge sharing.
Preferred Qualifications: The successful candidate brings a structured, detail-oriented approach and a hands-on mindset toward advancing the organization's data platform modernization.
- Bachelor's degree required.
- Minimum of 3+ years of experience in a data engineering or analytics engineering role.
- Strong hands-on experience with SQL, including complex transformations, performance tuning, and data modeling.
- Experience supporting ETL pipelines in an on-prem or hybrid data lake environment; exposure to PostgreSQL strongly preferred.
- Experience migrating or modernizing data platforms to Azure and/or Microsoft Fabric (Lakehouse, Pipelines, or related services).
- Solid understanding of modern data lake and warehousing architectures.
- Working knowledge of data engineering tools and languages such as SQL, Python, Spark, or similar technologies.
- Experience with at least one relational database platform (PostgreSQL, SQL Server, MySQL).
- Familiarity with data governance, data quality, or MDM concepts and tooling.
- Experience working in an agile, collaborative, cross-functional environment.
- Strong written and verbal communication skills; able to translate business requirements into technical solutions.
- Highly analytical, detail-oriented problem solver with a proactive, self-starter mentality.
- Comfortable working in evolving environments and translating conceptual requirements into pragmatic, scalable solutions.
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