Impac Exploration Services

Data Engineer - Real-Time Systems

Data Engineer - Real-Time Systems
Notice info
LocationWeatherford, OK
Job Typefull time
Remote
Oil and Gas

About This Job


Data Engineer - Real-Time Systems

Division: DATUM, Impac Exploration Services Location: Remote, Oklahoma City (OK), Houston (TX), San Jose (CA) Type: Full-Time

We're done with the "load it tonight, analyze it tomorrow" paradigm. At DATUM, decisions happen in milliseconds, not morning reports. We need a data engineer who believes streaming is the default, not the exception—someone who gets genuinely frustrated when people suggest "just run it as a nightly job."

Our data doesn't wait for convenient processing windows. It flows from sensors, cameras, and systems that never sleep. Your infrastructure will catch it, process it, and serve insights before traditional pipelines even know it arrived. If you think Kafka is table stakes and real-time inference is the only kind worth doing, we should talk.


What You'll Build

Streaming pipelines that handle millions of events per second
Infrastructure for real-time ML inference at the edge and core
Systems that treat historical data as streaming replay, not static files
Data architectures that scale
Fault-tolerant pipelines that keep flowing when hardware fails


Your Philosophy

The best data lake is a flowing river
Every millisecond of latency is a missed opportunity
Static ETL is where good data goes to get stale
If it's not real-time, it's not real


Technical Reality


Core streaming stack:

Apache Kafka/Pulsar/Redpanda (or better alternatives you'll introduce)
Flink/Spark Streaming for complex event processing
Time-series databases that can actually keep up (TimescaleDB, InfluxDB, or custom)
Languages: Python/Java/Rust—whatever makes it fast
Container orchestration without cloud vendor lock-in


What you won't use:

Traditional ETL tools that think "streaming" means every 5 minutes
Cloud services that hold your data hostage
Architectures that fall over when AWS hiccups


You're Our Person If

You've built streaming systems that stayed up when it mattered
"Eventually consistent" makes you uncomfortable
Real-time inference excites you more than data warehousing


Especially If

You've built on-premise streaming infrastructure that rivals cloud offerings
You've done inference at the edge before edge was cool
You understand hardware—from NVMe optimization to network tuning
You've migrated from batch to streaming and never looked back
You can make time-series data sing at scale
You believe data gravity is a solvable problem


Why This Matters


Your pipelines will power:

ML models making decisions while drill bits are turning
Computer vision processing streams from harsh environments
Analytics that prevent problems rather than explaining them later
Systems where "historical analysis" means 30 seconds ago

This isn't building dashboards for quarterly reviews. This is infrastructure for decisions that can't wait.


Growth Path

Today: Building streaming pipelines that embarrass traditional ETL Six months: Architecting systems that make cloud vendors nervous. One year: Publishing approaches that redefine industrial data processing.

When Databricks or Confluent tries to hire you, it'll be because you built something better than what they're selling.


Reality Check

You'll fight against decades of batch processing mindset. You'll optimize systems down to microseconds. You'll build infrastructure in places with challenging connectivity. You'll explain why "real-time" isn't just a buzzword.

But you'll also enable genuinely new capabilities. You'll prove that industrial systems can be as responsive as trading platforms. You'll build the foundation for AI that reacts as fast as physics demands.


Ready to Stream?

Show us streaming systems you've built that others said were impossible. Tell us why you believe batch processing is (mostly) dead. Share your vision for data infrastructure unchained from cloud providers.

We're looking for someone who sees "process nightly" and thinks "why wait?"

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