Role Overview
As a Data Operations Analyst, you will be the primary steward of our data ingestion engine. You won't just look at data; you will manage its movement, integrity, and transformation. Your mission is to ensure that complex real estate data from hundreds of external providers is perfectly mapped, standardized, and flowing through our AWS pipeline without interruption.
This role is ideal for someone who sits at the intersection of Data Analysis and Data Engineering—someone who loves automation, troubleshooting schema drift, and maintaining high-volume data systems.
Key Responsibilities
- Schema Mapping & Management: Manage the logic that translates raw MLS provider data into our internal standard. You will own the mapping files (often in JSON or YAML) that dictate how fields are ingested.
- Pipeline Monitoring & Troubleshooting: Proactively monitor daily ingestion jobs. When a pipeline breaks due to a provider changing their data format (schema drift), you are the first responder who identifies the shift and updates the mapping logic.
- Data Normalization: Write SQL and Python scripts to clean and normalize "messy" real estate data (e.g., converting "Half Bath: 1" and "Baths: 1.5" into a single, standardized format).
- Environment Maintenance: Use AWS tools to query raw landing zones (S3) and compare them against production outputs to ensure 100% data fidelity.
- Operational Automation: Identify repetitive manual mapping tasks and write scripts to automate them, reducing the "time-to-market" for onboarding new data sources.
Technical Requirements
- Advanced SQL: Beyond simple queries. You should be comfortable with window functions, complex joins, and querying semi-structured data (JSON/BSON) within a database.
- JSON/XML Proficiency: High comfort level navigating deeply nested structures. You should understand how to use JSONPath or similar logic to extract specific attributes.
- Scripting (Python/Bash): Ability to write and run scripts to transform data, call APIs, or automate batch updates.
- AWS Cloud Stack: * Athena/S3: For querying and inspecting data "at rest" in the data lake.
- CloudWatch: For monitoring pipeline logs and alerts.
- Glue/Lambda (Bonus): Understanding how serverless functions move data is a significant plus.
- Version Control: Experience using Git/GitHub to manage mapping configurations and script changes.
Real Estate Domain Knowledge (Highly Desired)
- RESO Standards: Knowledge of the RESO Data Dictionary and Web API.
- Data Lifecycle: Experience with the "Active-to-Sold" lifecycle of property listings and the nuances of MLS data rules.
Why this role is unique
- Not a "Dashboard" Role: You aren't building internal charts; you are building the core product data that powers the entire company.
- Technical Growth: You will work directly within an AWS-native environment, gaining experience that bridges the gap into Data Engineering.
- Critical Impact: In real estate, the data is the product. Your work directly impacts the accuracy of the platform for thousands of users.
