Data Engineer, Forward Deployed

Applied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable
abundance for a growing planet.

The hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational
data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls.

We’ve raised over $32 million, including one of the largest seed rounds for an
AI company in the UK. We’re just getting started

The Role

As our Data Engineer, you’ll architect and maintain pipelines that make high-frequency time-series, lab, and historian data into a scalable Lakehouse architecture , usable for both deep learning models and real-time LLMs. You’ll be working across AWS (EKS, S3, EBS, KMS, CloudWatch) and Databricks/PySpark , ensuring data is contextualised, synchronised, and optimised for both deep learning models and real-time LLM workloads.

This isn’t a traditional ETL role, you’ll be solving problems at the intersection of control systems, industrial data engineering, and AI enablement .

Technical Requirements

• Deep expertise in PostgreSQL (partitioning, indexing, query optimisation, storage design).

• Strong proficiency in Python for data processing, scripting, and pipeline orchestration.

• Hands-on experience with AWS (EKS, S3, EBS, IAM, KMS, CloudWatch, etc.)for secure and scalable data pipelines.

• Proven ability to work with Databricks and PySpark for large-scale distributed data processing.

• Familiarity with time-series industrial data (control systems, DCS/SCADA logs, process historians).

• Experience in unstructured data sync and management within hybrid cloud/on-prem environments.

• Bonus: Experience working as a data engineer in oil and gas or energy environments

• Bonus: Knowledge of streaming frameworks (Kafka, Flink, Spark Streaming) or MLOps stacks for data versioning and lineage.

Core Responsibilities

1. Ingest & Contextualise Data

• Ingest from OPC UA servers, process historians, IoT sensors, LIMS systems, alarms/events, and P&IDs.

• Map signals to their physical processes (tags, units, hierarchies) for interpretability in AI pipelines.

2. Data Movement & Accessibility

• Build pipelines that handle real-time streaming and batch ingestion into the Lakehouse.

• Manage synchronisation between historian archives, unstructured files, and AWS storage (S3/EBS).

• Orchestrate Databricks Lakeflow/Connectors for integrating data into Lakebase/Lakehouse.

• Handle secure, high-throughput transfers between historian archives and sandbox/live environments.

3. Change Tracking & Integrity

• Detect and manage schema changes, signal drift, and inconsistencies acrosstime.

• Implement lineage and audit trails across Spark/Databricks and AWS pipelines.

4. Data Preparation for AI

• Build and maintaindual pipelines:

  • Training→ large-scale historical data prep for time-series + LLM training.

  • Inference→ low-latency, real-time pipelines for anomaly detection, optimisation, and LLM search.

• Support heterogeneous AI workloads (time-series forecasting and retrieval-augmented LLMs).

5. Database Performance & Optimisation

• Tune PostgreSQLand sparkfor high-throughput time-series workloads (partitioning, indexing, query optimisation).

• Optimise pipelines for both fast analytical queries and high-efficiency model training.

• Deploy and manage data pipelines in AWS EKS (Kubernetes) with persisten tEBS-backed storage.

What Success Looks Like

• Live data streams are contextualised,queryable, and AI-ready.

• Schema changes and signal drift are detected and handled without breaking downstream workflows.

• Training and inference pipelines run smoothly in parallel, optimised for scale and latency.