Applied Computing
Data Engineer, Forward Deployed
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Applied Computing – Data Engineer
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, letting 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 usable for both deep learning models and real-time LLMs. You’ll work across AWS (EKS, S3, EBS, KMS, CloudWatch) and Databricks/PySpark, ensuring data is contextualised, synchronised, and optimised for AI workloads.
This isn’t a traditional ETL role—you’ll solve problems at the intersection of control systems, industrial data engineering, and AI enablement.
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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 & 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 in hybrid cloud/on-prem environments
Bonus:
- Experience working in oil and gas or energy environments
- Knowledge of streaming frameworks (Kafka, Flink) or MLOps stacks for data versioning and lineage
Core Responsibilities
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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
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Data Movement & Accessibility
- Build pipelines for real-time streaming and batch ingestion into the Lakehouse
- Manage synchronisation from historian archives, unstructured files, and AWS storage (S3/EBS)
- Orchestrate Databricks LakeFlow for Lakebase/Lakehouse integration
- Handle secure, high-throughput transfers between historian archives and sandbox/live environments
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Change Tracking & Integrity
- Detect and manage schema changes, signal drift, and inconsistencies across time
- Implement lineage and audit trails in Spark/Databricks and AWS pipelines
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Data Preparation for AI
- Build dual pipelines:
- Training: High-scaled 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 + retrieval-augmented LLMs)
- Build dual pipelines:
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Database Performance & Optimisation
- Tune PostgreSQL and Spark for high-throughput time-series workloads (partitioning, indexing, query optimisation)
- Optimise pipelines for fast analytical queries and high-efficiency model training
- Deploy and manage data pipelines in AWS EKS (Kubernetes) with persistent EBS-backed storage
What Success Looks Like
- Live data streams are contextualised, queryable, and AI-ready
- Schema changes and signal drift detected and resolved without breaking downstream workflows
- Training and inference pipelines run smoothly in parallel, optimised for scale and low latency
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