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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer, Data Platform - **Company:** Realty Professionals, LLC - **Location:** Austin, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Audit Trail, Code Review, Continuous Integration, Data Governance, Data Infrastructure, Data Transformation, Data Migration, Serialization, Software Debugging, Programming Tools, Distributed Systems, Elasticsearch, Identity and Access Management, Python (Programming Language), Key Management, Performance Tuning, Prism (Software), Regression Testing, DataOps, Data Streaming, TypeScript, Management of Software Versions, Workflow Management Systems, Data Ingestion, Large Language Models, Snowflake, KSQL, Core Api, Backend, Agentic-AI, Kotlin, Event Driven Architecture, Infrastructure Automation Frameworks, Low Latency, Deployment Automation, Avro, Apache Kafka, Data Management, New Relic (SaaS) - **Published:** October 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=bee59d9b930c5b3a ## About the Role * 8+ years of experience building backend infrastructure, APIs, distributed systems, data platforms, or developer tooling; equivalent experience is acceptable. * Bachelor's degree or equivalent experience. * Strong experience with event-driven systems and Kafka, including topics, partitions, consumer groups, delivery semantics, schema evolution, and operational troubleshooting. * Hands-on experience with AWS and infrastructure supporting data platforms; experience with MSK, MWAA, IAM, networking, monitoring, and managed services is highly valuable. * Experience operating one or more of Fivetran, Kafka Connect, Airflow, dbt, Snowflake, Schema Registry, KSQL, or comparable platforms in production. * Strong understanding of data ingestion, replication, orchestration, transformation, data contracts, lineage, observability, and pipeline reliability. * Proficiency in TypeScript/JavaScript, Python, Java, Kotlin, or another production backend language; the ability to work across more than one language is preferred. * Experience designing and supporting APIs, libraries, infrastructure automation, CI/CD, infrastructure as code, and deployment automation. * Familiarity with Avro or another schema and serialization system, data quality controls, compatibility management, and event-driven design. * Demonstrated ability to debug complex failures across distributed systems and communicate the diagnosis clearly. * Strong security instincts, including least privilege, secrets management, environment isolation, audit trails, and safe handling of restricted data. * Ability to document technical requirements and operating procedures so that other teams can successfully use the platform. * Ability to lead initiatives, make sound tradeoffs, and work effectively with product engineering, analytics, data science, security, and infrastructure partners. * Practical experience creating AI-assisted developer workflows, agentic automation, reusable AI skills, or tool-using LLM applications. * Understanding of AI safety and reliability practices, including grounded retrieval, permissions, human-in-the-loop approvals, observability, evaluation, and controlled production rollout. * Experience with Kafka/MSK upgrades, capacity planning, performance tuning, or multi-environment operations. * Experience building self-service platform products, internal developer platforms, or paved paths. * Experience with data observability, lineage, data contracts, incident automation, or support-ticket automation. * Experience with one or more of TypeScript, Python, and Kotlin client libraries or SDKs. Working style * Enthusiastically own outcomes end to end, from design through implementation, testing, deployment, documentation, and operational support. * Prefer reusable platform capabilities over one-off solutions. * Enjoy collaborating directly with end-users to understand and support their requirements and use cases. * Make tradeoffs explicit and optimize for reliability, security, simplicity, and user impact. * Collaborate openly, ask clarifying questions early, and communicate risks before they become incidents. * Treat production safety and data governance as requirements, not afterthoughts. ## Description 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. 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 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 escalate 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.