Senior Data Engineer (AI & Data Platform)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 52d ago
The date the source published, not the day we noticed it (2026-08-20). Last seen at its source 3h ago.
We have tracked this listing since 5 Oct 2026 (5 days). The employer's own board has carried it every time we have read it, most recently 3 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Not stated
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Ashby employer's own board first seen 5d ago · last seen 3h ago
The listing
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:
Internal AI Agents: Creating curated, governed company data that our internal 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 Agentic Systems 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 cloud infrastructure (warehouse, ingestion, transformation, and orchestration).
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.
Construct Data Pipelines: Build reliable ELT/CDC pipelines from production Postgres and backend services using cloud-native streaming/ingestion tooling and dbt for transformation.
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.
Establish Orchestration & CI/CD: Set up workflows using a modern orchestrator (Airflow, Dagster, or Cloud Composer), alongside data CI/CD environments and automated testing.
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 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.
Demonstrable LLM & AI Data expertise: Proven track record of building embeddings pipelines, managing vector databases (e.g., pgvector, Pinecone, Qdrant, Weaviate, Vertex AI Vector Search), and architecting RAG data plumbing or feature stores that feed production AI/LLM systems.
Deep understanding of how AI agents consume data, including data retrieval strategies, context window management, semantic search optimization, and grounding LLMs in structured enterprise data.
Expert SQL and deep PostgreSQL experience, alongside strong hands-on expertise with major cloud data warehouses (e.g., BigQuery, Snowflake, Redshift, or Databricks).
Proficient in Python for building scalable data pipelines, custom ML/AI data tooling, and integrations.
Strong ELT/ETL design using dbt and modern orchestration frameworks (Airflow, Dagster, or Prefect).
Hands-on experience with modern cloud data infrastructure across major providers (GCP, AWS, or Azure data services, object storage, and event streaming/messaging queues).
Strong grasp of data governance: Enterprise-grade data quality, lineage, observability, and strict PII controls suitable for financial and sensitive domain data.
Desirable Pluses:
Direct experience building data layers that production LLM agents depend on.
Experience with Streaming / Change Data Capture (CDC) at scale and near-real-time serving.
Experience with ML feature engineering, MLOps, or cloud AI ecosystems.
Familiarity ingesting data from backend APIs (.NET/Go/Python) and tracking events from React/React Native clients.
A background in Fintech, payments, or the charitable-giving/nonprofit domain.
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