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What is **REI? REI is a pioneering B2B data and SaaS platform that empowers professional real estate investors to generate consistent leads from Cold Call, SMS, and Direct Mail without spending more. We leverage AI, machine learning, and advanced analytics to identify homeowners likely to sell at a discount, and we use a proven strategy to maximize our clients' ROI. As part of our network, we also operate alongside three sister companies: DMForce, Recruit, and CRM, each contributing specialized services to the real estate sector. Core Values at REI: Find a Better Way: We dig until we understand the real problem, then improve or innovate to solve it. Focused on the 20% that drives the biggest result. Have Each Other's Back: We work as a team. We support, step in, and go the extra mile to create a great experience for each other and our clients. Honor Your Word: We honor our promises, hold ourselves accountable, and do the right thing even when nobody's looking. Own Your Growth: We're obsessed with personal growth and take pride in the work we deliver. Intrinsically motivated, we hold ourselves to a high standard and never coast. Bring Your A-Game: We take care of our recovery, health, and family time, and that's why we show up focused, energized, and ready to perform at our best. Role Overview We are looking for a Senior Data Engineer to own the design, reliability, and cost-efficiency of our AWS data platform. You will build and optimize the PySpark/EMR pipelines that power our deal-scoring engine (Apollo), our property valuation product (BestEstimate), and our permits and roofing intelligence lines; harden our Hudi-based medallion lakehouse; and operate the data-serving APIs our clients and internal teams depend on. This is a hands‐on senior role with real ownership: you will make architecture decisions, carry the cloud budget for the data platform, enforce our data quality standard, and partner directly with Data Science to turn models into reliable production systems. If you like nationwide‐scale data, pragmatic engineering, and seeing your pipelines drive real revenue, this seat is for you. Key Responsibilities: Own Big Data pipelines end to end. Design, build, and optimize PySpark ETL/ELT pipelines on Amazon EMR and AWS Glue that process nationwide, county‐partitioned property data (First American, BuildZoom permits, market comps) on daily and monthly cadences. Run our lakehouse. Operate and evolve our Bronze → Silver → Gold data lake on S3 with Apache Hudi and the AWS Glue Data Catalog, queried through Athena, including schema contracts, partitioning strategy, compaction, and performance tuning. Orchestrate and automate. Build reliable orchestration with AWS Step Functions, EventBridge, and Lambda; make reruns, backfills, and failure recovery boring and documented. Enforce data quality. Implement and extend our Data QA Audit Standard: layer contracts, write‐audit‐publish gating, quarantine flows, drift monitoring, and actionable Slack alerting, so bad data never reaches a client list. Operate production databases and APIs. Manage Aurora PostgreSQL and DynamoDB workloads, and run data‐serving APIs (API Gateway, SQS‐backed async workers), such as our Address Resolution Service, to production SLAs with dashboards and runbooks. Own cloud cost. Monitor, report, and reduce the AWS data‐platform bill (EMR cluster sizing, Glue/Lambda usage, S3 lifecycle, Athena scan costs) as a first‐class engineering responsibility. Ship infrastructure as code. Define infrastructure with Terraform and CloudFormation, delivered through GitHub Actions CI/CD with tests, linting, and coverage gates, we run a disciplined PR, branch‐policy, and code‐review culture. Partner with Data Science. Build the feature pipelines, training datasets, and serving paths behind our ML scoring and valuation models (scikit‐learn/XGBoost‐family stack), and co‐own the handoff contracts between DS and DE. Document like a pro. Maintain runbooks, architecture docs, and data dictionaries (Confluence) so any teammate can operate what you build. Qualifications: 4+ years of hands‐on data engineering with large‐scale distributed data systems and a track record of production ownership (not just development). Advanced PySpark performance tuning, partitioning strategy, and cost‐aware cluster sizing on real workloads (EMR or equivalent). Strong Python clean, tested, production‐grade code (we use pytest, ruff, mypy, and coverage gates in CI). Deep AWS experience EMR, Glue, Lambda, S3, Athena, Step Functions, EventBridge, IAM, and VPC networking; comfort operating (not just deploying to) these services. Advanced SQL complex analytical queries, query optimization, and data modeling on both a warehouse/lake engine (Athena/Presto) and PostgreSQL. Lakehouse experience hands‐on production work with at least one open table format (Apache Hudi strongly preferred; Iceberg or Delta Lake also valued) and medallion‐style architecture. Data quality mindset experience implementing validation, quality gates, monitoring, and incident response for production data. Infrastructure as Code Terraform and/or CloudFormation in a CI/CD workflow. English and Spanish professional working proficiency in both (B2+). Bachelor's degree in Computer Science, Systems Engineering, Data Engineering, or equivalent practical experience. Nice to Have: Real estate, property, or geospatial data experience (county/FIPS‐partitioned datasets, address standardization, parcel/permit data). Building or operating public/internal data APIs (API Gateway, SQS, DynamoDB caching, SLAs). Observability tooling (CloudWatch, Grafana dashboards, structured logging). ML‐adjacent engineering: feature pipelines, training‐data reproducibility, model‐serving data paths. Modern Python tooling (uv, Poetry) and monorepo/template‐driven repo governance. Agile/SCRUM experience and a habit of writing documentation others actually use. Why Join Us? At REI, you'll play a key role in shaping the future of data analytics for the real estate investment industry. As a team member, you'll have the opportunity to lead initiatives, work with cutting‐edge technologies, and collaborate with a dynamic team passionate about innovation and results. What We Offer: Competitive Base Compensation Profit Share Bonus Flex PTO (up to 26 days per year) Home Office Upgrade Bonus HMO Bonus Full‐time Remote Work Opportunity for growth and team‐building potential Ongoing support and budget to develop new skills Join us at REI and contribute directly to revolutionizing the real estate investment landscape! #J-***-Ljbffr
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.