Enterprise Data Architect

Job description

# **Data Architect** **In-house . Full Time . On-Site at BGC, Taguig. Dayshift Schedule About the Role** We're looking for a Data Engineer to own our company's single source of truth for data. This isn't a back-office infrastructure role — it's the foundation that makes every team's decisions better, from Sales and Finance to the product teams building our newest tools. You'll be responsible for the full data platform: getting data in cleanly from every system we use, structuring it into one trusted warehouse, and making sure that no matter where someone looks across the company, the numbers always match. You'll also help make that data easy enough to use that our teams (and our AI tools) can self-serve most of their own analysis, with you and your team focused on keeping the foundation rock solid. **What You'll Own** - Data pipelines: build and run reliable, automated pipelines that bring data in from every system we use — our in-house portals, HubSpot, QuickBooks, Jira, and more — into one central warehouse. - One source of truth: maintain a single, trusted customer and business record so that every team and every system shows the same numbers, every time. - Data quality: catch and fix data issues before anyone downstream notices them. You own the monitoring, not the complaints. - Proactive delivery: work directly with teams across the company to understand what data they need, and have it ready — clean, structured, and refreshed in near real-time — before they have to ask. - Safe change management: review any change to how a system structures its data before it ships, so changes never quietly break the warehouse or any report built on it. - Self-serve enablement: structure and document data well enough that our teams, and our AI tools, can query it directly and get the right answer without needing a custom report built for them. - Team leadership: manage and grow 2 data engineers. Support a dedicated data analyst with the access and platform tools they need to deliver deeper, custom insights for leadership and the teams they work with. **What Success Looks Like** - Pull the same number from two different systems — they always match. - Every team has the data they need, when they need it, without feeling blocked. - Data lands clean and on time, with zero quality issues reaching a team before you've already caught and fixed them. - Our AI tools and internal teams can ask a data question directly and get an accurate answer, without a custom report being built for them. - When any system changes how it structures data, nothing breaks downstream — because you reviewed it first. **How Success Is Measured** Performance is tracked against a few simple, outcome-based numbers — not a long list of technical metrics. | | | | | --- | --- | --- | | **KPI** | **Target** | **What It Means** | | Data Availability | 99%+ | Data pipelines run on schedule and the warehouse is up and accessible when teams need it. | | Data Accuracy | 99%+ | Numbers match across every system. Zero data quality issues reach a team before they've already been caught and fixed. | | Data Accessibility | 99%+ | Teams and AI tools can get the data they need, when they need it, through self-serve access — without being blocked or waiting on a custom report. | - 4+ years of experience in data engineering, with a track record of owning a production data warehouse or platform end to end. - Strong SQL skills and hands-on experience with modern data pipeline and ETL/ELT tools (e.g. dbt, Airflow, Fivetran, or similar). - Experience working with a layered warehouse architecture (medallion architecture — bronze/silver/gold). - Hands-on experience with Databricks and Azure cloud services (e.g. Azure Data Factory, Azure Data Lake, Azure SQL). - Comfort integrating data from a mix of in-house systems and third-party platforms (CRM, finance, project management tools). - Experience exposing data via APIs, or working with teams that consume data through APIs. - Some experience with, or strong interest in, the data needs of ML/AI pipelines — you don't need to build models, but you understand what clean, structured data they require. - People management experience, or strong readiness to manage and grow a small team. - Clear communicator who's comfortable partnering directly with non-technical teams to understand what they need. **Nice to Have** - Experience working in a fast-growing or scaling company where the data platform was built (or rebuilt) from the ground up. - Familiarity with Python for data tooling and automation. - Experience setting data governance standards or leading data quality initiatives. **Why This Role Matters** The data platform you'll own is the foundation for everything we're building next — from AI-powered tools to new internal products that depend on clean, real-time data. If you want to be the person who makes sure the whole company can trust its numbers, this is that role.