Staff Software Engineer, Data Platform

News Corp; Austin (TX); In loco; USD 150.000 - 210.000; Tempo pieno

Descrizione del lavoro

Realtor.com in Austin, TX, part of News Corp, seeks a hands-on Staff Software Engineer to advance the Data Ingestion Platform. You will design, build, and operate scalable ingestion, streaming, replication, orchestration, and transformation capabilities used by many teams.

You will own platform APIs, libraries, and reference implementations, work with Kafka/MSK, Airflow, dbt, and Snowflake, and improve reliability, developer experience, and security across the data platform.

Mansioni

• 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 TypeScript, 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 dbt platform capabilities, including project conventions, macros, testing, materializations, restricted-data patterns, and Airflow integration.
• Develop reusable TypeScript, Python, and/or Kotlin libraries and reference implementations for producing, consuming, validating, and moving data.
• Contribute to Snowflake integration patterns, data exports, toolsets, and secure access paths without taking ownership of stakeholder-specific data modeling.
• Take pride in ensuring delivery, quality, availability, low latency, cost efficiency, and operational reliability for our platform capabilities.
• Instrument systems with actionable metrics, logs, traces, alerts, runbooks, and service-level objectives.
• Diagnose data issues across the full path from source to serialization, topic, connector, and Snowflake destination.
• Participate in incident response, root-cause analysis, post-incident improvements, and production support rotations.
• Apply least-privilege access, environment separation, secrets management, auditability, and secure-by-default platform design.
• Improve platform health monitoring and standardize operational workflows across Fivetran, Kafka/MSK, Airflow, dbt, connectors, and Snowflake.
• Balance reliability, delivery speed, developer experience, operational simplicity, and cost when making architecture decisions.
• Create clear onboarding paths, templates, examples, documentation, and diagnostics that help teams self-serve.
• Partner closely with internal users to understand their ingestion and transformation needs and guide them to the appropriate platform path.
• Improve discoverability, lineage, data contracts, and metadata through integration with the data catalog.
• Clearly communicate technical requirements, tradeoffs, operating boundaries, and migration plans to engineers and stakeholders.
• Inspire and mentor fellow engineers, raising the technical bar through constructive design reviews, code reviews, pairing, and knowledge sharing.
• Create practical agentic workflows that automate repetitive platform and developer-support work, such as onboarding, schema validation, DAG and dbt scaffolding, connector checks, pipeline diagnostics, incident triage, and documentation updates.
• Build reusable AI skills, tools, and workflows that interact with platform APIs, repositories, tickets, observability systems, and documentation while preserving appropriate access controls.
• Design grounded AI experiences that use authoritative platform knowledge, return structured findings, cite or link to source documentation, and elevate safely when confidence is low.
• Develop agents that can reason across pipeline boundaries, correlate evidence, identify likely failure points, and produce concise remediation guidance or actionable tickets.
• Add automated checks and evaluation harnesses for agent behavior, including correctness, safety, permissions, tool-use reliability, hallucination resistance, and regression testing.
• Use AI to improve engineering throughput without weakening review, change-management, production-safety, or data-governance controls.
• Contribute reusable patterns for prompt/context design, tool calling, workflow orchestration, human approval, audit logging, and continuous improvement of AI-enabled platform operations.

Descrizione del lavoro

Staff Software Engineer, Data Platform

Location: Austin, TX, United States

Category: Technology

Job ID: 3352

Description

Recognized as the No. 1 site trusted by real estate professionals, Realtor.com® 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, Realtor.com® 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 Realtor.com.

Our 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.

• 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 TypeScript, 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 dbt platform capabilities, including project conventions, macros, testing, materializations, restricted‑data patterns, and Airflow integration.

• Develop reusable TypeScript, Python, and/or Kotlin libraries and reference implementations for producing, consuming, validating, and moving data.

• Contribute to Snowflake integration patterns, data exports, toolsets, and secure access paths without taking ownership of stakeholder‑specific data modeling.

  Reliability, security, and operations

• Take pride in ensuring delivery, quality, availability, low latency, cost efficiency, and operational reliability for our platform capabilities.

• Instrument systems with actionable metrics, logs, traces, alerts, runbooks, and service‑level objectives.

• Diagnose data issues across the full path from source to serialization, topic, connector, and Snowflake destination.

• Participate in incident response, root‑cause analysis, post‑incident improvements, and production support rotations.

• Apply least‑privilege access, environment separation, secrets management, auditability, and secure‑by‑default platform design.

• Improve platform health monitoring and standardize operational workflows across Fivetran, Kafka/MSK, Airflow, dbt, connectors, and Snowflake.

• Balance reliability, delivery speed, developer experience, operational simplicity, and cost when making architecture decisions.

  Developer experience and enablement

• Create clear onboarding paths, templates, examples, documentation, and diagnostics that help teams self‑serve.

• Partner closely with internal users to understand their ingestion and transformation needs and guide them to the appropriate platform path.

• Improve discoverability, lineage, data contracts, and metadata through integration with the data catalog.

• Clearly communicate technical requirements, tradeoffs, operating boundaries, and migration plans to engineers and stakeholders.

• Inspire and mentor fellow engineers, raising the technical bar through constructive design reviews, code reviews, pairing, and knowledge sharing.

  AI‑assisted engineering and agentic automation

• Create practical agentic workflows that automate repetitive platform and developer‑support work, such as onboarding, schema validation, DAG and dbt scaffolding, connector checks, pipeline diagnostics, incident triage, and documentation updates.

• Build reusable AI skills, tools, and workflows that interact with platform APIs, repositories, tickets, observability systems, and documentation while preserving appropriate access controls.

• Design grounded AI experiences that use authoritative platform knowledge, return structured findings, cite or link to source documentation, and elevate safely when confidence is low.

• Develop agents that can reason across pipeline boundaries, correlate evidence, identify likely failure points, and produce concise remediation guidance or actionable tickets.

• Add automated checks and evaluation harnesses for agent behavior, including correctness, safety, permissions, tool‑use reliability, hallucination resistance, and regression testing.

• Use AI to improve engineering throughput without weakening review, change‑management, production‑safety, or data‑governance controls.

• Contribute reusable patterns for prompt/context design, tool calling, workflow orchestration, human approval, audit logging, and continuous improvement of AI‑enabled platform operations.

  What You’ll Bring