Data Engineer

Position Type: Full-time, exempt, fully remote

Hours: 35 hours per week

Salary Range: $105,000–$135,000 annually

Reports to: Vice President of Technology

At OneKey, when we hire an individual to fill a position, it is not to simply perform a function, but to contribute to the culture of the entire organization. OneKey MLS is an Equal Opportunity Employer, and our valued team members possess different strengths, experiences, and backgrounds, yet share a passion for collaborating to solve problems. All our employees' points of view add value and contribute to our organization's success.

The Data Engineer is responsible for designing, developing, and maintaining scalable data pipelines and data integration solutions that support business operations and strategic decision-making. This role partners with business stakeholders, software developers, and IT team members to ensure data is accurate, accessible, secure, and optimized for reporting and analytics. This is an exempt, full-time position. The hours are 35 hours per week and report to the VP of Technology. This position is fully remote.

OneKey MLS is seeking a motivated mid-level Data Engineer to turn real estate data into trusted datasets for market reports, interactive websites, and future AI applications. You will build and maintain ETL/ELT pipelines and help develop a data lakehouse that retains raw and historical data while providing structured, curated data for reporting and analytics.

Working with Technology, Product, and business teams, you will reproduce existing vendor report calculations, deliver consistent monthly, quarterly, and annual comparisons, and prepare fast reporting datasets for a React developer to display on the web. We value curiosity, ownership, attention to detail, and a willingness to learn.

• Build data pipelines and lakehouse datasets. Develop ETL/ELT workflows to ingest real estate data from APIs, databases, and files. Clean, standardize, deduplicate, and transform source records into reusable datasets. Maintain raw, historical, and curated layers with documented data models.

• Reproduce and validate market reports. Review existing vendor reports, source data, and available methodology with business stakeholders. Reproduce metric definitions, filters, and calculations, investigate differences, and reconcile results before release. Document assumptions when vendor methodology is unavailable.

• Deliver consistent period comparisons. Prepare monthly, quarterly, and annual reports from shared data and calculation rules, including side-by-side comparisons and percentage changes against the same period in the prior year, such as September versus the previous September. Handle incomplete periods, missing values, and zero denominators consistently.

• Preserve historical reporting accuracy. Create nightly snapshots of listing and market data to support point-in-time reporting. Manage incremental loads, historical backfills, late-arriving updates, and corrections through repeatable processes that avoid duplicate records.

• Prepare fast web reporting data. Build summary tables, materialized views, or other precomputed datasets for supported combinations of geography, property type, price range, bedrooms, square footage, year built, sale type, and reporting period. Support metrics such as median sales price, listing counts, closed sales, days on market, and inventory.

• Partner on application delivery. Provide documented schemas, queries, and data contracts for the React developer and API layer. Coordinate cache refreshes with tools such as Redis. Balance precomputation, storage, and query performance while preserving accurate results for custom filters and metrics such as medians.

• Maintain reliable and secure data. Monitor pipeline runs, freshness, completeness, and data quality. Implement validation checks, failure alerts, retries, and recovery procedures. Optimize queries and indexes, maintain version-controlled code, and document lineage, metric definitions, and operating procedures.

• Prepare data for AI applications. Create curated datasets, metadata, and documented access patterns for approved AI search, analysis, and other use cases. Apply data licensing, privacy, access, and retention requirements in partnership with the Technology team.

• Collaborate across teams, recommend practical improvements, and perform other job-related duties as assigned.

Requirements

• Approximately 2–5 years of relevant experience in data engineering, database development, analytics engineering, or a comparable role, with demonstrated delivery of working data pipelines.

• Strong SQL skills, including joins, window functions, aggregations, query tuning, and relational data modeling.

• Strong analytical and organizational skills. Ability to work independently in a remote team, communicate progress, ask questions, and follow through on commitments.

• Hands-on experience building and maintaining ETL/ELT workflows, scheduled jobs, incremental loads, and data validation checks.

• Proficiency in Python or another language used for data processing and integration, plus experience with APIs, Git, and troubleshooting production data issues.

• Experience with relational databases and exposure to cloud data platforms, data warehouses, or data lakes. Understanding of historical snapshots and dimensional modeling.

• Ability to translate report requirements into documented calculations, validate results against reference reports, and explain discrepancies to technical and business audiences.

• Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.

Preferred Qualifications

• Experience with real estate or MLS data, market statistics, listing status history, or geographic reporting.

• Experience with PostgreSQL, SQL Server, cloud object storage, or a warehouse or lakehouse platform.

• Experience with workflow orchestration, Redis or similar caching, precomputed reporting datasets, and data delivery for web applications.

• Familiarity with Azure, AWS, or Google Cloud, business intelligence tools, or preparing governed data for AI applications.

• Candidates do not need experience with every preferred technology. We welcome applicants who bring strong fundamentals, practical delivery experience, and a willingness to learn.

• These duties, requirements, and skills do not constitute a complete statement of the position. Other job-related essential and non-essential duties may be assigned.

(These essential job duties, requirements and skills are not to be construed as a complete statement of all duties, requirements, and skills. This position will be required to perform other job-related essential and non-essential duties, requirements, and skills as required).