QA Data Automation Engineer | Orca AI · Teeming.ai
Orca AI
Orca AI is a maritime technology company founded in 2018 that provides AI and computer vision-based solutions to enhance situational awareness and safety in shipping operations. Their key products…
Orca AI is a maritime technology company founded in 2018 that provides AI and computer vision-based solutions to enhance situational awareness and safety in shipping operations. Their key products…
Core products: SeaPod (automated lookout) and FleetView (fleet monitoring)
Total disclosed funding: ~$111M (Series B $72.5M announced May 6, 2025)
Scale signals: 1,200+ vessels booked; 80M+ nautical miles of data
Company Overview
Problem Domain
Maritime situational awareness, safety, and autonomy for commercial shipping.
Founded
2018
Industry
Maritime Transportation
Tech Stack
Amazon CloudFront
Apache
Cloudflare
Cloudflare CDN
Cloudflare Network Error Logging
CrUX Top 50m
DNSSEC
Envoy
Google Cloud CDN
IPv6
Funding Track Record
Series A- 2021-04-21
13000000
Series A announced April 21, 2021
Growth / follow-on- 2024-05-23
23000000
Follow-on round described between Series A and Series B
Series B- 2025-05-06
72500000
Series B announced May 6, 2025 bringing total disclosed funding to ~$111M
Investor Signal
“Led by Brighton Park Capital in Series B with participation from existing investors including Ankona Capital and Hyperlink Ventures”
Founders
What we do
Join the Team
QA Data Automation Engineer
HybridTel Aviv District
Hybrid • Tel Aviv District
We are looking for a
QA Data Automation Engineer
to join our
Data Team
in a dynamic and challenging role, providing critical test coverage for Orca’s data pipelines, reporting layers, and analytics solutions.
In this role, you will be responsible for validating data integrity end-to-end — from raw ingestion and transformation layers to dashboards and downstream consumers. You will design and maintain automated tests to ensure accurate, reliable, and scalable data systems.
Key Responsibilities
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Develop and maintain
automated QA tests
for data pipelines, transformations, and data products.
Perform and execute manual QA testing such as functional, regression, and sanity testing of applications, dashboards, and backend systems.
Validate data flow across the system, including:
ingestion, transformations, reports/dashboards
.
Perform
data quality testing
(completeness, consistency, accuracy, timeliness, schema validation).
Write and execute
SQL-based tests
to validate logic, joins, aggregations, metrics, and anomalies.
Build automation frameworks and validation scripts using
Python
.
Work closely with Data Engineers and Analytics/BI stakeholders to define test coverage and acceptance criteria.
Investigate failures and data issues, providing clear RCA and actionable bug reports.
Document test plans, test scenarios, expected results, and automation coverage.
Track issues in
Jira
, including reproducible steps and supporting evidence.
Continuously improve QA processes for better monitoring, reliability, and faster releases.
Requirements
4+ years of QA experience
, including experience with automation or data validation flows.
QA Methodology knowledge (STP, QA cycles)
Proven experience testing
data systems
(ETL/ELT pipelines, DWH, analytics platforms, BI reports).
Strong
SQL skills
– ability to write complex queries for validation and troubleshooting.
Strong
Python skills
– writing scripts/tests for automated validations (pytest is a plus).
Hands-on experience working with
Data Lakes / Data Warehouses
such as
Snowflake (preferred)
.
Strong understanding of bug lifecycle management using
Jira
.
High attention to detail, critical thinking, and problem-solving mindset.
Excellent communication skills and ability to work cross-functionally in a fast-paced environment.
Nice to Have
Knowledge of cloud platforms (AWS).
Experience working with large-scale datasets, partitions, and performance tuning.
