Data Integration Engineer

Northwood Investors LLC•Denver, CO

4d•$100,000 - $115,000•Onsite

About The Position

The Data & AI Integration Engineer is based in Denver and reports to the VP of Data & Analytics. This role sits at the center of Northwood’s data platform build-out: a Snowflake and AWS estate, an enterprise-wide Claude deployment, and a growing set of AI-enabled capabilities — Snowflake Cortex, retrieval over the firm’s document corpus, and agentic workflows that put analytics directly in the hands of investment and asset management teams. Scope spans data modeling, ETL/ELT development, API and application integration, metadata-driven orchestration, data observability, and Power BI delivery. This is a high-ownership seat on a five-person Data & Analytics team, not a ticket queue. The engineer owns pipelines and integrations end to end, working within architectural patterns set alongside the VP of Data & Analytics — and takes on progressively more architectural and standards-setting responsibility as platform and domain depth build. Modern data engineering on a small team, with direct visibility into how the firm invests and operates. Northwood’s data is real estate data. The strongest candidates pair data engineering and cloud architecture depth with genuine curiosity about the domain — how an NOI walk is built, how a CAM reconciliation ties out, why chart-of-account integrity determines whether a portfolio roll-up can be trusted. Prior real estate, accounting, or financial-data exposure is valuable; curiosity and rigor matter more.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a related field. Equivalent combinations of education and relevant experience will be considered.
2–4 years writing complex SQL, including query tuning and performance optimization.
2–4 years with cloud data services on AWS S3, Lambda, ECS/Fargate, API Gateway, and related integration patterns.
2–4 years developing and supporting Python, JavaScript, APIs, SDKs, and cloud-based data services.
2–4 years building and orchestrating ETL/ELT pipelines using modern tooling such as Snowpark or AWS-native orchestration or equivalent traditional ETL platforms (SSIS, Informatica).
Working knowledge of relational data modeling and data warehouse design concepts.
Working familiarity with version control, CI/CD practices, repository conventions, and automated testing.

Nice To Haves

Experience building solutions in the financial services domain, with an understanding of financial instruments, transactions, and positions.
Experience supporting or using property management and ERP software such as MRI and Yardi.
Experience designing metadata-driven orchestration, dependency management, or infrastructure-as-code patterns for production pipelines.
Experience with AI-enabled data tools, document intelligence, enterprise search, agentic workflows, or LLM-powered automation.
Exposure to Snowflake Cortex, Snowpark Container Services, data application development, observability platforms, or enterprise AI governance workflows.
Experience with business intelligence and data visualization tools such as Power BI or Tableau.
Advanced experience with Office 365, particularly Excel (Power Pivot, Power Query) and Power Automate.

Responsibilities

Owns new and existing ETL/ELT workflows, accelerating delivery of new datasets, process automation, and complex production loads.
Designs and manages reusable cloud-native integration patterns across AWS, Snowflake, S3, Lambda, ECS/Fargate, API Gateway, Snowpark, and related services.
Builds standardized, metadata-driven orchestration — table-level and source-system execution, runtime result capture, dependency tracking, Snowflake metadata tables, and operational health monitoring.
Executes migration of legacy ETL workloads onto shared infrastructure, reusable repository templates, automated deployment patterns, and modern S3-to-Snowflake loading processes.
Improves platform reliability and observability through standardized logging, metadata capture, health checks, performance tracking, alerting, and issue remediation workflows.
Builds automation and AI-enabled tooling to accelerate ingestion, migration, monitoring, documentation, and internal platform support.
Partners with multidisciplinary teams to gather functional business requirements, scope analytical projects, manage expectations and deadlines, and translate needs into technical specifications.
Develops data models and custom SQL views for ingestion, integration, and presentation-layer consumption, structured for private equity reporting and analytics.
Supports the development, deployment, performance, lineage, and integration of BI and data visualization tools, including Power BI semantic models and reporting workflows.
Manages change to existing data warehouse reports and deliverables based on business feedback.
Collaborates with internal staff and third-party vendors on application data integrations.
Maintains and extends documentation standards for code, procedures, mapping documents, architectural patterns, operational runbooks, and reusable implementation templates.
Builds depth across AWS, Power BI, Python, SDK usage, repository structure, CI/CD practices, and data loading strategies, and shares working patterns with teammates.
Partners with business stakeholders to apply and help shape data governance policies.
Weighs near-term delivery against long-term scalability, reliability, and maintainability, escalating significant architectural trade-offs to the VP of Data & Analytics.