University degree in Computer Science, Data Science, Statistics, or equivalent practical experience.
Proficient in SQL (complex analytical queries, CTEs, window functions) and Python (pandas, boto3 preferred).
Hands-on experience with Amazon QuickSight (or Quick Suite) — ideally 2+ years building dashboards, datasets, calculated fields, RLS, and Paginated Reports.
Strong analytical mindset and proven ability to manipulate and interpret large datasets.
Requirements:
Build, maintain, and improve Quick Suite dashboards, analyses, datasets, and Paginated Reports for aviation KPIs (revenue, fleet utilization, delays, crew duty, etc.).
Use SQL to query and validate data from Redshift and underlying PostgreSQL sources; perform data cleaning, outlier detection, and light transformation.
Own parts of the ETL pipeline: monitor refreshes, debug failures, and help prepare for incremental / future Spark-based processing; segment and prepare data to support operator-isolated dashboards and targeted reports.
Communicate insights clearly through visualizations, annotated screenshots, and concise one-pagers; collaborate with Product Owner and management to translate business requirements into BI deliverables.
Job description
This is a remote position.
We have an exciting new opportunity for a Data Scientist / BI Engineer to join the BRIGHT team and help evolve FL3XX’s embedded business intelligence platform.
You will play a key role in improving our historical BI dashboards (powered by Amazon Quick Suite) and making our aviation data more actionable for mid-to-large operators. The role is autonomous, hands-on, and gives you real ownership over a product used daily by charter, fractional, and aircraft management companies.
This position is based in Vienna, Austria, or fully remote.
Your Responsibilities
Build, maintain, and improve Quick Suite dashboards, analyses, datasets, and Paginated Reports for aviation KPIs (revenue, fleet utilization, delays, crew duty, etc.).
Use SQL to query and validate data from our Redshift layer and underlying PostgreSQL sources.
Perform data cleaning, outlier detection, and light transformation.
Create and manage QuickSight namespaces, users, folders, permissions, and Row-Level Security rules.
Own parts of the ETL pipeline: monitor refreshes, debug failures, and help prepare for incremental / future Spark-based processing.
Segment and prepare data to support operator-isolated dashboards and targeted reports.
Identify, investigate, and resolve data quality issues and discrepancies (e.g., duplicate records, freshness alerts).
Add and test new calculated fields and data sources to meet evolving reporting needs.
Communicate insights clearly through visualizations, annotated screenshots, and concise one-pagers to sales, product, and management teams.
Collaborate with the Product Owner and management to translate business requirements into practical BI deliverables.
Requirements
University degree in Computer Science, Data Science, Statistics, or equivalent practical experience.
Strong analytical mindset and proven ability to manipulate and interpret large datasets.
Proficient in SQL (complex analytical queries, CTEs, window functions) and Python (pandas, boto3 preferred).
Hands-on experience with Amazon QuickSight (or Quick Suite) — ideally 2+ years building dashboards, datasets, calculated fields, RLS, and (bonus) Paginated Reports.
Good understanding of data governance, data quality, and multi-tenant isolation concepts.
Excellent written and spoken English — you can explain complex topics clearly and concisely.
Curious, eager to learn, and proactive about acquiring new skills.
Ability to summarize findings and create intuitive, insightful visualizations (portfolio or examples strongly preferred).
Comfortable working collaboratively with product, engineering, sales, and management teams.
Structured, rigorous, and detail-oriented in delivery — you finish what you start.
Experience with ETL processes and working with large datasets (Redshift, S3, or similar cloud warehouse experience a plus).
Nice-to-Have (but very welcome)
Domain knowledge in aviation, fleet management, or travel operations.
Experience with Amazon Q (topics, synonyms, generative BI) or similar NLQ tools.
Familiarity with data prep/joins in QuickSight or similar BI tools.
Exposure to boto3 automation for QuickSight assets or S3/Redshift operations.
Previous work on paginated / pixel-perfect reporting.
