Senior Data Engineer, Data Strategy and Architecture

CenterSquare Investment Management; Conshohocken (Montgomery County); On-site; USD 120,000 - 170,000; Full time

Job summary

CenterSquare Investment Management is seeking a Senior Data Engineer to architect and operate our data platform, driving ingestion, data modeling, and governance for analytics and AI initiatives. You will build robust ELT pipelines, optimize Snowflake performance, and own security controls while collaborating with product, data strategy, and engineering teams.

The role emphasizes hands-on development in a data-driven environment, cross-functional collaboration, and components critical to our

Qualifications

• Bachelor's degree in CS/Engineering/IS or equivalent.
• 5–7 years of experience building data pipelines and data platforms in production.
• Expert-level SQL and strong Python proficiency; production-grade code.
• Proven experience building ELT pipelines with Dagster, Airflow, Prefect, or equivalent.
• Solid understanding of medallion architecture (Bronze–Silver–Gold).
• Experience integrating external data providers, APIs, and SaaS platforms.
• Experience implementing automated testing, data quality monitoring, and CI/CD for data pipelines.
• Solid understanding of data security and access control in financial services.

Responsibilities

• Design and develop robust ingestion pipelines for internal systems, third-party apps, APIs, fund administrators, custodians, and external data providers.
• Build reusable, high-quality ELT pipelines with version control, automated testing, and CI/CD workflows.
• Develop curated, semantically governed data models across Bronze, Silver, and Gold layers for analytics and AI.
• Continuously optimize Snowflake performance, scalability, and cost efficiency, including RBAC governance.
• Manage and maintain CenterSquare's Snowflake platform and configuration.
• Monitor platform health and performance; establish escalation paths for production data issues and lead root cause analysis.
• Develop automation to reduce operational overhead and enable scaling.
• Maintain technical documentation and engineering runbooks.
• Partner with Product Owner to translate requirements into ingestion specifications and pipelines; collaborate across technical and non-technical audiences.
• Align platform delivery with the firm's data and AI roadmap.

Skills

SQL

Python

ELT pipelines

Dagster

Airflow

CI/CD

RBAC

Data governance

Communication

Investment data familiarity

Education

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field

Tools

Snowflake

dbt

Airflow

Dagster

Prefect

Atlan

Alation

Job description

Senior Data Engineer - Data Strategy & Architecture

Location: Conshohocken, PA

Company Overview

Founded in 1987, CenterSquare Investment Management is an independent, employee-owned real asset manager focused on listed real estate, private equity real estate and private real estate debt. As a trusted fiduciary, our success is firmly rooted in aligning our interests with those of our clients, partners and employees. CenterSquare is headquartered in suburban Philadelphia, with offices in New York, Los Angeles, London and Singapore. With 14 billion in assets under management (July 2026), CenterSquare and its affiliates are proud to manage investments on behalf of some of the world's most well-known institutional and private investors.

Job Overview

The Senior Data Engineer is a core technical contributor on CenterSquare's emerging data platform team, responsible for building, maintaining, and continuously improving the pipelines, data models, and infrastructure that power the firm's analytics, reporting, and AI strategy. Working in close partnership with the Director of Data Strategy & AI and the Data Product Owner, this role translates defined architectural standards into reliable, well-governed data infrastructure. The Senior Data Engineer owns day-to-day engineering and operations of CenterSquare's Snowflake platform, executing against clear architectural direction to deliver data products the firm can trust. This is a high-ownership, hands-on engineering role for someone who excels at building in ambiguous environments, takes pride in data quality and pipeline reliability, and wants to be a foundational contributor to a platform being built from the ground up.

Key Responsibilities

Data Engineering

• Design and develop robust ingestion pipelines for internal systems, third-party applications, APIs, fund administrators, custodians, and external data providers, including alternative and real estate-specific data sources.
• Build reusable, high-quality ELT pipelines using modern engineering practices, including version control, automated testing, and CI/CD deployment workflows.
• Develop curated, semantically governed data models across Bronze, Silver, and Gold layers optimized for analytics, reporting, and downstream AI applications.
• Continuously optimize Snowflake performance, scalability, and cost efficiency, including warehouse configuration, query optimization, and RBAC governance.

Platform Operations & Cloud Infrastructure

• Manage and maintain CenterSquare's Snowflake platform and configuration
• Monitor platform health, availability, and performance; establish clear escalation paths for production data issues and lead root cause analysis on incidents.
• Develop automation that reduces operational overhead, improves platform reliability, and enables the data organization to scale efficiently.
• Maintain comprehensive technical documentation, engineering runbooks, and standards that support knowledge sharing and platform continuity.

Data Quality & Governance

• Implement automated data quality testing, validation, and observability tooling to ensure high reliability and trust across the data platform.
• Support the data ownership model by enforcing naming conventions, maintaining data lineage records, and collaborating with the Product Owner to define acceptance criteria for each data domain.
• Contribute to governance documentation including access control definitions and policy materials that support AI-readiness and regulatory defensibility.

Collaboration & Stakeholder Engagement

• Partner closely with the Product Owner to translate documented business requirements and data source inventories into ingestion specifications and pipeline builds.
• Translate business needs from investment, operations, and client-facing teams into well-governed technical solutions, operating effectively across both technical and non-technical audiences.
• Collaborate with the Director of Data Strategy & AI to align platform delivery with the firm's broader data and AI roadmap.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent professional experience.
• 5-7 years of experience building data pipelines and data platforms in production environments.
• Strong hands-on experience with Snowflake, including warehouse management, performance tuning, RBAC, cost monitoring, and data sharing patterns.
• Expert-level SQL and strong Python proficiency; experience writing production-grade, testable code.
• Proven experience building ELT pipelines using modern orchestration tools ( Dagster , Airflow, Prefect, or equivalent).
• Solid understanding of medallion architecture patterns (Bronze to Silver to Gold) or equivalent layered data architecture.
• Experience integrating external data providers, APIs, and SaaS platforms.
• Demonstrated experience implementing automated testing, data quality monitoring, and CI/CD practices for data pipelines.
• Solid understanding of data security and access control design in a financial services or regulated environment.
• Experience in investment management, asset management, or financial services, with working knowledge of investment data flows including fund administration, custodian data, market data feeds, or alternative asset data structures.
• Strong written and verbal communication skills with the ability to work effectively across technical and business stakeholders.

Preferred Qualifications

• Snowflake SnowPro Core certification.
• Experience with dbt or similar transformation frameworks.
• Hands-on experience building data infrastructure that supports AI workloads, including structured data preparation for LLM-based and agentic workflows.
• Familiarity with data catalog, metadata management, and data lineage solutions (Atlan, Alation, or equivalent).
• Experience working with alternative asset or real estate-specific data sources, including property-level data, GP/LP reporting structures, or REIT data.