Data Sr Engineer

The Data Engineering Lead owns the end-to-end delivery of REI's data engineering capability. From pipeline architecture and data modelling through to deployment, monitoring and governance, this person is accountable for the full lifecycle of every data product runs in production. The role requires equal strength in hands-on technical execution and project delivery: the ability to design a Snowflake data vault is as important as the ability to take a complex data initiative from requirements to live deployment without losing momentum.

‒ Own the delivery of data engineering initiatives from requirements and solution design through development, testing, deployment and handover, without dependency on others to close the loop.

‒ Translate business and analytical requirements into well-scoped engineering plans with clear milestones, dependencies and delivery commitments.

‒ Manage delivery risk proactively: identify blockers early, propose pragmatic solutions and communicate progress to stakeholders with clarity.

‒ Maintain delivery momentum across multiple parallel initiatives, prioritising effectively when scope or resource constraints arise.

‒ Produce thorough project documentation covering architecture decisions, pipeline design, data dictionaries and operational runbooks.

‒ Design, build and maintain scalable, reliable data pipelines on Snowflake using Python and dbt, applying dimensional modelling, data vault or lakehouse patterns appropriate to each data domain.

‒ Architect robust ETL and ELT frameworks that standardise how ingests, transforms and serves data across financial, property, portfolio and investor data sources.

‒ Write production-grade Python for pipeline orchestration, custom transformations, API integrations and automation workflows.

‒ Build and maintain dbt projects: models, tests, documentation, seeds, snapshots and macros, applying dbt best practices throughout.

‒ Ensure all pipelines meet defined SLAs for freshness, latency and completeness, with alerting in place before failures affect consumers.

‒ Integrate operational data sources and third-party market data feeds into the Snowflake data platform.

‒ Design and own the data model across all domains: financial performance, property data, portfolio analytics, investor reporting and operational metrics.

‒ Apply appropriate modelling techniques (dimensional, data vault, OBT or hybrid) based on consumption patterns, performance requirements and governance needs.

‒ Maintain a canonical data model that serves BI, AI/ML and operational consumers consistently, avoiding duplication and semantic drift.

‒ Review and approve data model changes proposed by other engineers, enforcing naming conventions, referential integrity and version control.

‒ Work with the Data Intelligence Lead to align the physical Snowflake data model with the semantic layer and Power BI dataset structures.

‒ Design, deploy and maintain Airflow DAGs for pipeline orchestration, scheduling, dependency management and failure handling across all data workflows.

‒ Build a comprehensive job monitoring framework: pipeline health dashboards, SLA breach alerting, failure notifications and on-call escalation runbooks.

‒ Define retry logic, dead-letter handling and circuit-breaker patterns for resilient pipeline operation in production environments.

‒ Conduct regular pipeline performance reviews, identifying and resolving bottlenecks, redundant processing and inefficient query patterns.

‒ Establish on-call practices and incident response procedures for critical data pipeline failures, including root-cause analysis and post-incident documentation.

‒ Own data engineering CI/CD pipelines: automated testing, linting, deployment workflows and environment promotion from development through staging to production.

‒ Define and enforce Git branching strategies, pull request standards, code review practices and release management processes for the data engineering team.

‒ Embed automated data quality checks, schema validation and regression tests into CI/CD pipelines so that no untested code reaches production.

‒ Maintain infrastructure-as-code for data engineering components, ensuring environments are reproducible and configuration drift is eliminated.

‒ Lead the adoption of software engineering best practices across the data team: modular code, DRY principles, meaningful documentation and peer review culture.

‒ Design and deliver the Snowflake serving layer that powers Power BI reporting, ensuring datasets are optimised for query performance, row-level security and incremental refresh.

‒ Translate reporting requirements into well-engineered data products that BI consumers can rely on without workarounds.

‒ Build reusable dbt models and mart layers that serve multiple BI and analytical use cases without duplication of transformation logic.

‒ Support the rationalisation of legacy reporting pipelines, replacing brittle ETL processes with governed, tested dbt transformations.

‒ Apply data governance standards to all engineering work: lineage documentation, metadata tagging, ownership assignment and Collibra catalogue alignment.

‒ Implement Snowflake security controls: dynamic data masking, row-access policies, role-based access control and network policy configuration.

‒ Maintain data retention, archiving and deletion workflows in compliance with CBRE enterprise data policies and applicable regulatory requirements.

‒ Document data lineage end to end for all critical pipelines, enabling regulatory and audit queries to be answered with evidence rather than estimates.

‒ Lead, mentor and develop a team of data engineers, setting technical standards, conducting code reviews and growing individual capability.

‒ Define the data engineering roadmap, prioritising initiatives in alignment with REI technology and business strategy.

‒ Act as the technical authority for data engineering decisions: architecture reviews, tooling evaluations, vendor assessments and platform design choices.

‒ Build a team culture of ownership, quality and continuous improvement, where engineers take pride in what they ship and how they ship it.

‒ Communicate engineering progress, risks and technical constraints to non-technical REI leadership in plain, outcome-oriented terms.

‒ Minimum 7 years in data engineering, analytics engineering or a closely related discipline, with clear progression in technical scope and seniority.

‒ Demonstrated track record of delivering data engineering projects end to end: from requirements through architecture, build, deployment and operations.

‒ Prior experience leading or technically mentoring a data engineering team.

‒ Experience in financial services, real estate, investment management or another complex, multi-source data environment.

‒ Expertise in Snowflake, Python, dbt, ETL frameworks, SQL, CI/CD, BI, data modeling, data governance.

‒ Exposure to AI/ML data engineering: feature store design, training dataset pipelines, MLOps data requirements and LLM grounding data preparation.

‒ Knowledge of streaming or near-real-time data patterns: Kafka, Kinesis or Snowflake dynamic tables.

‒ Relevant certifications: Snowflake SnowPro Advanced Data Engineer, dbt Certified Developer, Apache Airflow certification or equivalent.

‒ Experience with cloud infrastructure beyond Snowflake: Azure Data Factory, Databricks, AWS Glue or equivalent.