This is not a junior role.
At SentraAI Data Engineers work on live enterprise-scale data systems that are built to run scale and be trusted. The pipelines you build will be used monitored and relied upon. When they break you are expected to help fix them.
If you are not ready to take responsibility for production systems this role is not for you.
The Operating Context
SentraAI delivers data and AI platforms inside large complex enterprise environments with high expectations around reliability quality and governance.
You will be working on:
A modern cloud lakehouse architecture
Batch and real-time data pipelines
Multiple upstream systems and downstream consumers
Data that supports reporting operations and AI workloads
This is not greenfield. The systems are live and evolving.
Your Accountability
You are accountable for the quality and reliability of the data pipelines you build.
That includes:
Developing Spark-based batch pipelines under guidance from senior engineers
Contributing to streaming pipelines and CDC workflows
Implementing data quality checks and validations
Supporting production issues and pipeline failures
Writing clear maintainable testable code
Communicating clearly when something is at risk or not working
Ownership starts early and increases with capability.
Data pipelines using Apache Spark (PySpark preferred)
Bronze and Silver layer transformations in a lakehouse architecture
SQL-based transformations and aggregations
Basic streaming or near-real-time pipelines
Data quality checks using agreed frameworks
You are expected to actively use AI tools to improve development speed testing quality and documentation within SentraAIs defined governance standards.
You have only worked with static datasets or dashboards
You have not supported data pipelines in production
You expect requirements to always be fully defined
You avoid ownership when things fail
You are uncomfortable receiving direct technical feedback
We are explicit because the environment demands it.
What Strong Looks Like
Within 612 months:
Pipelines you own are stable and predictable
Data quality issues are identified and resolved early
You can debug failures with limited supervision
Senior engineers trust your contributions
You take on progressively more responsibility
This is how performance is measured.
What We Expect You To Be Strong At
Python for data engineering
SQL for transformations and analysis
Apache Spark fundamentals and distributed processing concepts
Basic understanding of data modelling and quality
Version control and collaborative development
Clear professional communication in English
Hands-on exposure to enterprise-scale data systems
Mentorship from senior engineers and architects
Clear progression based on demonstrated capability
Real responsibility early not shadow work
Career capital that scales beyond SentraAI
Final Note
SentraAI is not a training ground.
It is a place to become a serious data engineer through responsibility delivery and accountability.
If you want to grow by building systems that matter we should talk.
This listing is sourced from a third-party job board. Applying will redirect you to the original posting.
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