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 GCP. While we run a production Postgres database, we do not yet have an 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:
Internal AI Agents: Creating curated, governed company data that our internal Claude-based agent systems can reason over reliably.
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 AI Engineer who builds the orchestration and agent logic on top of it.
Key Responsibilities
Build the Analytics Platform (Greenfield): Design and build our first analytical data platform on GCP (warehouse, ingestion, transformation, and orchestration).
Construct Data Pipelines: Build reliable ELT/CDC pipelines from production Postgres and .NET/C# services using GCP-native tooling (Datastream, Dataflow, Pub/Sub) and dbt for transformation.
Establish Orchestration & CI/CD: Set up workflows using Cloud Composer/Airflow or Dagster, alongside data CI/CD environments and automated testing.
Model Data for AI & Analytics: Create dimensional models and a documented semantic/metrics layer to serve dashboards, data analysts, and internal Claude agents with unified definitions.
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 data.
Own Governance & Compliance: Implement data validation, lineage, observability, and PII controls appropriate for sensitive financial, donor, payments, and cross-border tax data.
Collaborate Across Teams: Partner with the AI Systems Engineer and the .NET backend team to instrument product events and ship customer-facing insights.
Qualifications
6+ years of Data Engineering experience, including at least one greenfield warehouse/platform build you owned end-to-end.
Expert SQL and deep PostgreSQL experience, alongside strong experience with cloud data warehouses (BigQuery is ideal).
Strong ELT/ETL design using dbt and a modern orchestrator (Airflow/Cloud Composer or Dagster).
Hands-on with the GCP data stack (BigQuery, Datastream, Dataflow, Pub/Sub, Cloud Storage).
Proficient in Python for building data pipelines and custom tooling.
Demonstrable LLM/AI data experience: Embeddings pipelines, vector stores (e.g., pgvector, Vertex AI Vector Search, Pinecone), RAG plumbing, or feature stores feeding production ML/LLM systems.
Strong grasp of data quality, lineage, observability, and PII data governance.
Desirable Pluses:
Familiarity ingesting data from .NET/C# backends and tracking events from React/React Native clients.
Experience with Streaming / Change Data Capture (CDC) at scale and near-real-time serving.
Experience with ML feature engineering, MLOps, or the Vertex AI ecosystem.
A background in Fintech, payments, or the charitable-giving/nonprofit domain.
Direct experience building data layers that production LLM agents depend on.
Our Benefits
Remote or Hybrid Work Options
Private Health Insurance, including dental care
Additional Holidays after your 1st and 5th year
Sponsored Training & Certifications
Employee Referral Bonuses
Multisport Card – fully covered
Fun Office Space with relaxation zones and free parking
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Key team members
Omid Rezvani
Mirela Stankusheva
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