Data Engineer
Posted 20 Aug 2026
Advertised as “Senior Data Engineer (AI & Data Platform)”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
No salary listed for this role.
Mobile Wave Solutions is a professional services company specializing in software development as a service. With a team of over 120 engineers, we deliver scalable, high-quality software that empowers our global clients to innovate and grow. We value collaboration, technical excellence, and a pragmatic approach to solving complex problems.
About the Role
We’re looking for a Senior Data Engineer to be a foundational, greenfield hire and build our analytical data platform from the ground up on modern cloud infrastructure. While we run a production Postgres database, we do not yet have a dedicated analytical data platform—and that is the heart of this role.
You will design and build our data foundation, turning it into the trusted core that powers our agentic AI on two distinct fronts:
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Internal AI Agents: Creating curated, governed company data that our internal agent systems can reason over reliably.
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Customer-Facing AI Features: Building the modeled data pipelines and feature stores behind the predictive and prescriptive insights shown on customer dashboards.
You are an expert data engineer who genuinely understands how modern LLM agents consume data, embeddings, and feature stores. In this role, you will completely own the data and retrieval layer, partnering closely with our Senior Agentic Systems Engineer who builds the orchestration and agent logic on top of it.
Key Responsibilities
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Build the Analytics Platform (Greenfield): Design and build our first analytical data platform on cloud infrastructure (warehouse, ingestion, transformation, and orchestration).
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Develop the AI Data Layer: Build and maintain the retrieval substrate, embeddings pipelines, and vector storage (RAG data plumbing) to keep internal and external agents grounded in fresh, accurate data.
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Construct Data Pipelines: Build reliable ELT/CDC pipelines from production Postgres and backend services using cloud-native streaming/ingestion tooling and dbt for transformation.
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Model Data for AI & Analytics: Create dimensional models and a documented semantic/metrics layer to serve dashboards, data analysts, and internal AI agents with unified definitions.
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Establish Orchestration & CI/CD: Set up workflows using a modern orchestrator (Airflow, Dagster, or Cloud Composer), alongside data CI/CD environments and automated testing.