Level 05 - Software / Data Operations Engineer

Our Company:

At Abyss, our mission is to create innovative AI and robotics solutions, removing the need to put people at risk in dangerous jobs. From deep-sea rovers to our recent Moon to Mars Australian Government grant, and emerging technology in Agriculture, there is no challenge that Abyss will shy away from.

A global team collaborating across Australia, America, and Pakistan consists of passionate problem solvers who love working on cutting-edge technology while maintaining a focus on environmental impact, safety, and cost. 

You’ll get to work on complex challenges with a team of experts in software engineering, machine learning, data processing, and robotics.

In short, you'll get to work with some absolute LEGENDS, have fun, and get to work on cutting-edge software, in a company that is rapidly growing and has a REAL impact on the world, not just pushing numbers around.

Position Summary

The Data Operations Engineer executes and supports daily data-processing workflows within the Software Operations team. This role investigates pipeline failures, identifies whether issues originate from data or system/flow bugs, collaborates with senior developers to resolve and learn debugging paths, and performs rapid scripting and transformation tasks (CSV, JSON, point-cloud) within a cloud and orchestration-tool environment (e.g., Prefect).

TKS Statements

Below are the discrete Task, Knowledge, and Skill statements aligned with the role.

Tasks (T)

Run daily data-processing workflows — Execute production workflows each day, ensuring processing jobs start, complete, and deliver on time.

Monitor workflow execution and detect anomalies — Observe job statuses, alerts, and logs to detect failures or unusual patterns.

Investigate pipeline failures to isolate root cause — Determine if a failure is due to data quality, metadata inconsistency, transformation bug, or orchestration error.

Collaborate with senior developers on debugging sessions — Shadow or join senior devs in root-cause analysis of complex failures, and apply remediation.

Re-run failed jobs and validate corrected output — After fixes, restart jobs, confirm correct outputs, and log results.

Perform ad-hoc scripting tasks for rapid turnaround (“BlackOps”) — Script transformations, cleanup or merging of CSV/JSON files and handle point-cloud file preparation as needed.

Maintain operational logs, trackers, and documentation — Update task-tracking systems (e.g., ClickUp), workflow execution logs, and document debugging learnings for SOP updates.

Operate and manage cloud-based compute and orchestration infrastructure — Use and maintain compute/storage resources in the cloud, work within orchestration tool flows (e.g., Prefect flows).

Knowledge (K)

Knowledge of data-processing workflow concepts (ingestion, transformation, validation, output).

Knowledge of pipeline orchestration tools and how tasks, triggers, and retries function (e.g., Prefect, Airflow).

Knowledge of cloud compute and storage infrastructure (e.g., Google Cloud Platform, buckets, VMs, job scheduling).

Knowledge of data formats and their characteristics (CSV, JSON, point-cloud files, metadata structures).

Knowledge of debugging and root-cause-analysis methodologies (log inspection, stack trace interpretation, data vs code differentiation).

Knowledge of version control workflows (e.g., Git) and how code changes affect production pipelines.

Knowledge of operational workflow management (SOPs, monitoring, alerts, task-tracking tools).

Skills (S)

Skill in executing and monitoring data-processing workflows via orchestration tools.

Skill in interpreting logs, alerts, and job execution statuses to identify anomalies.

Skill in diagnosing whether a failure is due to data issues (e.g., malformed input, missing metadata) or processing/flow bugs.

Skill in writing and modifying Python scripts for data transformation (CSV/JSON) and point-cloud preparation.

Skill in operating cloud-based compute/storage resources (launching jobs, managing buckets, handling permissions).

Skill in collaborating with development and operations teams, communicating clearly about debugging outcomes and next steps.

Skill in documenting operational flows, troubleshooting steps, and updating SOPs or workflow trackers.

Key Requirements

Minimum of 4-5 years of experience in data operations, pipeline monitoring, or data engineering support.

Proficiency in Python scripting for data manipulation.

Experience working with CSV, JSON, and ideally point-cloud file formats.

Familiarity with cloud platforms and data workflow orchestration tools (e.g., Prefect).

Strong analytical and debugging mindset; able to distinguish data failures vs. code/flow failures.

Excellent collaboration and communication skills; able to work with senior developers and operations teams.

Comfortable in an operationally-focused, fast-paced environment with daily delivery demands.

Nice to Have

Prior experience in 3D scanning, CAD/point-cloud workflows.

Experience with other workflow tools (Airflow, Dagster) and CI/CD pipelines.

Familiarity with task-tracking and project management tools (e.g., ClickUp).

Qualification

Bachelor's or Master’s degree in Computer Science, Software Engineering, or a related field.