ETL Developer & QA Specialist
Duration: Long-term
Location: NYC, NY
Position Summary:
The Data Engineer / Business Intelligence & QA Specialist will support enterprise‑scale Business Intelligence and Data Warehousing initiatives for Sirius XM Satellite Radio. This role is responsible for end‑to‑end ETL development, data analysis, data validation, and testing across multiple source systems supporting analytics, reporting, marketing insights, and royalty management.
The role involves close collaboration with BI developers, data architects, QA teams, and business stakeholders to ensure high data quality, accurate reporting, and reliable analytics platforms.
Key Responsibilities
Data Engineering & ETL Development
- Design, develop, and maintain end‑to‑end ETL pipelines using Talend Studio and Informatica based on business and technical requirements.
- Develop and maintain stored procedures to support complex transformations and optimized data loads.
- Support and enhance Data Warehouse ETL processes, ensuring accurate population of datamarts and analytics layers.
- Develop and maintain Databricks notebooks using Python, PySpark, and SQL for data processing and analytics.
- Perform data extraction, transformation, and loading from multiple source systems including Marketing, Subscriber Management, OEM, Royalty, and Terrestrial Operations.
Data Analysis & Validation
- Perform detailed data analysis across multiple OEM and consumption data sources to support reporting and analytics needs.
- Validate source‑to‑target data mappings, ensuring all database fields are loaded correctly without truncation or data loss.
- Verify data integrity, job dependencies, and transformation logic against mapping and design documents.
- Perform record‑level reconciliation and checksum validation to ensure record counts and completeness.
- Execute complex SQL queries to validate data accuracy across Teradata, Redshift, and Aurora databases.
- Validate data at Unix level to ensure pipeline execution and delivery accuracy.
Testing & Quality Assurance
- Perform manual testing of web applications, mobile applications, and radio device setups.
- Validate track listening time and consumption metrics through device‑level and application‑level testing.
- Develop and execute automated test scripts using Python and Selenium.
- Perform comprehensive ETL testing, including data completeness, transformation logic, and dependency validation.
- Log, track, and manage defects using HP ALM and JIRA, ensuring proper resolution and closure.
Reporting & BI Support
- Support reporting and analytics initiatives using Cognos BI, leveraging MSAS cubes built from curated datamarts.
- Ensure high‑quality data availability for business reporting, marketing analytics, and royalty reporting.
- Collaborate with BI teams to troubleshoot data issues impacting reports and dashboards.
Agile & Collaboration
- Participate actively in Scrum ceremonies, including stand‑ups, sprint planning, reviews, and retrospectives.
- Work within Agile and Kanban delivery models using JIRA.
- Collaborate with cross‑functional teams including Data Architecture, BI, QA, and Business stakeholders.
Required Skills & Qualifications
Technical Skills
- Strong experience in ETL development using Talend Studio and Informatica
- Strong SQL skills with experience writing complex queries
- Hands‑on experience with Databricks (Python, PySpark, SQL)
- Experience with Teradata, Amazon Redshift, and Amazon Aurora (PostgreSQL)
- Experience in Unix/Linux environments
- Experience with Cognos BI and MSAS cubes (reporting layer)
Testing & QA Skills
- Experience in ETL testing and data validation
- Experience in manual testing across web, mobile, and device platforms
- Experience in automated testing using Python and Selenium
- Familiarity with HP ALM, DB Visualizer, and JIRA
Methodologies & Tools
- Strong understanding of Data Warehousing concepts
- Experience working in Agile / Scrum / Kanban environments
Preferred Qualifications
- Experience in media, broadcasting, subscription, or royalty management domains
- Experience analyzing OEM and consumption data
- Exposure to AWS‑based data platforms
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