Data Engineer

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.

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.

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.

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.

• 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.