Software Engineer, Data Platform

Position: Staff Software Engineer, Data Platform
Reference: 3352

Posted:

Location:

Austin, TXCompany:
Realtor.com Staff Software Engineer, Data Platform

Location:

Austin, TX, United States Category:
Technology Job  Now Description Recognized  as the No. 1 site trusted by real estate professionals,  has been at the forefront of online real estate for over 25 years, connecting buyers, sellers, and renters with trusted insights and expert guidance to find their perfect home. Through its robust suite of tools,  not only makes a significant impact on the real estate industry at large, but for consumers, navigating the biggest purchase they will make in their life, by providing a user experience that is easy to use, easy to understand, and most of all, easy to make decisions.

Join us on our mission to empower more people to find their way home by breaking barriers to entry, making the right connections, and building confidence through expert guidance.

Staff Software Engineer, Data Ingestion Platform About the Role We are excited to welcome a hands-on Staff Software Engineer to our Data Movement and Transformation team within the Data organization! In this role, you will help design and operate the paved-path platform that empowers product engineering, analytics, data science, and business teams to move and transform data seamlessly and reliably across  team champions foundational capabilities spanning real-time streaming, batch ingestion, data replication, orchestration, transformation, schemas, connectors, platform libraries, observability, and developer self-service.

We are looking for a collaborative engineer who thrives across platform infrastructure and developer-facing tooling, takes pride in production outcomes, and makes it effortless for teams to onboard and operate data workflows.

Team platform charter

We are dedicated to building managed, highly reusable paths for:

Real-time and batch data ingestion through Prism/Data Collector and related APIs.Kafka-based streaming on AWS Managed Streaming for Apache Kafka (MSK), including topics, producers, consumers, Kafka Connect, KSQL, and streaming integrations.

Data contracts and schema governance using Avro and Schema Registry, including compatibility, validation, and lineage metadata.

External-system replication and integration through Fivetran and Kafka Connect, including secure connection management and operational support.

Scheduled and batch workflows through Apache Airflow on AWS Managed Workflows for Apache Airflow (MWAA).Data transformation and modeling through dbt, including integration with Airflow, Snowflake, and the data catalog.

Snowflake lakehouse access patterns, data exports, reusable toolsets, and platform libraries.

Observability, alerting, incident response, pipeline health checks, and self-service troubleshooting across ingestion, streaming, connectors, Airflow, dbt, and Snowflake.

Documentation, onboarding, paved paths, reference implementations, and developer experiences that help teams adopt the platform without bespoke support

We focus on crafting and operating shared capabilities and reference tooling. Rather than building each team's custom producer, consumer, connector, or dbt model directly, we delight in empowering teams with the platform, guardrails, templates, libraries, and guidance they need to build safely and efficiently!

What You'll Do Platform engineering

Design, build, and operate scalable ingestion, streaming, replication, orchestration, and transformation capabilities used by many teams.

Develop and improve platform APIs, services, libraries, templates, and paved paths for streaming, batch, bulk, and external data ingestion

Build and maintain ingestion APIs and client libraries in Type Script, Kotlin, and Python, supporting streaming, batch, bulk, and external data-ingestion use cases.

Operate and evolve Kafka/MSK infrastructure, including topics, partitions, replication, retention, throughput, consumer groups, access controls, and upgrades.

Support Kafka Connect and sink integrations for systems such as Snowflake, S3, New Relic, and Elasticsearch; improve connector reliability, provisioning, and troubleshooting.

Build and maintain Schema Registry and Avro-based data contract workflows, including schema validation, compatibility, versioning, and safe evolution.

Build and maintain KSQL or equivalent near-real-time transformation patterns where appropriate.

Improve Airflow/MWAA orchestration patterns, DAG templates, scheduling, backfills, retries, dependency management, and operational reliability.

Improve…