Data Engineer, AI & Data Platforms
Who we hire
Humble, hungry, and smart. You enjoy building the foundations that analytics,
machine learning, and AI applications depend on. You get energy from solving
difficult engineering problems and making complex systems simpler, more reliable,
and easier to operate.
You break large data challenges into manageable pieces. You care about
architecture, performance, quality, and maintainability. You use AI tools as part of
your workflow, but you validate everything through engineering rigor and testing.
You can discuss platform design with architects, troubleshoot production issues with
engineers, and explain technical trade-offs to business stakeholders. We can put you
in front of a client on week one.
The role
You build enterprise-scale data platforms that power analytics, machine learning,
GenAI, and business-critical reporting.
You own major components end to end: ingestion pipelines, transformation
frameworks, data models, orchestration, monitoring, and platform integrations. You
work as part of a small pod alongside Data Scientists, Full Stack Engineers,
Architects, and client stakeholders.
This is data engineering first. Reliability, scalability, governance, and operational
excellence matter as much as shipping features.
You work directly with clients, not behind a ticket queue.
What a real week looks like
Build a new ingestion framework that processes customer data from multiple
enterprise systems into BigQuery.
Debug a production incident where a Dataflow job succeeds in development but fails
in a client's restricted GCP environment.
Design a Lakehouse architecture using BigQuery, Dataproc, and Databricks and
explain the trade-offs to client stakeholders.
Partner with a Data Scientist to productionize a Vertex AI model and automate
feature pipelines.
Review pipeline performance and investigate why costs increased after a schema
change.
Mentor a Grade 8 engineer who is struggling with Spark optimization and pipeline
design.
Use AI development tools throughout and catch the places where the AI is
confidently wrong.
What you'll do
Design and build scalable batch and real-time data platforms.
Own the architecture of your component: ingestion patterns, orchestration, lineage,
monitoring, security, governance, and resilience.
Develop enterprise-grade ETL and ELT pipelines across cloud and hybrid
landscapes.
Integrate enterprise systems such as ERPs, CRMs, identity platforms, APIs,
streaming systems, and legacy applications.
Build data products that support analytics, machine learning, GenAI, RAG, and
agentic AI applications.
Design and maintain data lakes, warehouses, and Lakehouse platforms.
Run production data workloads with monitoring, observability, cost optimization, and
operational controls.
Take proof-of-concepts into production with testing, deployment pipelines, data
quality validation, and governance controls.
Present architecture decisions to client teams and explain trade-offs in plain
language.
Review code, mentor Grade 8 engineers, and raise the engineering quality bar
across the pod.
Expertise required
Data engineering
Strong Python and SQL.
ETL and ELT architecture, data modeling, pipeline orchestration, and distributed
processing.
Data quality, lineage, reliability engineering, and observability.
You can independently design, build, and operate production-grade data platforms.
Google Cloud Platform (Preferred)
Strong hands-on experience with:
BigQuery
Dataflow
Dataproc
Cloud Storage
Pub/Sub
Cloud Composer (Airflow)
Vertex AI integrations
Cloud Functions
Cloud Run
IAM and security services
You understand cloud-native design patterns and production operations on GCP.
DataOps
CI/CD for data pipelines.
Infrastructure as Code.
Automated testing.
Release management.
Monitoring, logging, and alerting.
You build systems that teams can operate reliably at scale.
Enterprise integration
REST APIs, messaging systems, event-driven architectures, and enterprise
applications.
Kafka, Pub/Sub, Event Hub, or similar technologies.
Relational and NoSQL databases.
You have integrated with real enterprise environments and understand operational
constraints.
AI and ML platform enablement
Data foundations for Vertex AI and machine learning workloads.
Feature engineering pipelines.
Model-serving integrations.
Support for GenAI, RAG, vector search, and agentic AI systems.
You understand how data platforms enable AI applications.
AI-assisted engineering
Copilot, Cursor, Claude Code, Gemini Code Assist, or similar tools as part of your
daily workflow.
A deliberate approach to AI-assisted development, testing, debugging, and
documentation.
You teach effective AI-assisted engineering practices to others.
Communication
Clear technical writing and documentation.
Architecture presentations.
Stakeholder management.
Comfortable discussing technical trade-offs with client teams and executives.
What a successful candidate looks like
You have 5 or more years of experience building enterprise data platforms.
You may have come from a cloud-native engineering team, a data platform
organization, a consulting business, or a product company operating large-scale
data systems.
You naturally discuss trade-offs:
"We chose BigQuery instead of a self-managed warehouse because it reduced
operational complexity and improved scalability."
You talk openly about engineering failures and what you learned from them.
You have side projects, certifications, open-source contributions, or experiments that
demonstrate curiosity.
You can describe your AI-assisted development workflow in practical detail.
You ask sharp questions about business outcomes before proposing technical
solutions.
You give credit to teammates easily.
What success looks like
First 30 days: You understand the client's data ecosystem, identify meaningful
improvements, and take ownership of a pipeline or platform component.
First 90 days: You independently own a production workload, improve performance
or reliability, and actively contribute during client discussions.
At 12 months: You own a platform capability that multiple teams rely on. You have
helped develop at least one Grade 8 engineer. Clients trust your recommendations
and seek your input on architecture decisions.
Why join
Work on enterprise-scale data and AI platforms for leading global clients.
Build the foundations that enable analytics, machine learning, and GenAI
applications.
Work alongside architects, software engineers, data scientists, and AI specialists.
Build on modern GCP platforms including BigQuery, Vertex AI, Dataflow, and
Dataproc.
Use the best AI development tools and help shape how engineering teams use
them.