What are the responsibilities and job description for the Data Analytics Engineer position at SES?
Top 5 Technical Skills:
- Python
- SQL
- API /. SDLC
- Data Insights and Visualization
- Financial Services Domain Knowledge
Job Description:
Seeking an experienced Data Analytics Engineer / Business UAT Tester with 7 years of analytics, monitoring, visualization, production support, and developer collaboration experience, including 5 years validating business requirements, data outputs, reports, APIs, and applications before production deployment. Hands-on with GenAI-assisted data extraction, prompt-guided validation, report-ready JSON generation, summarization, trend analysis, anomaly detection support, and validation of generated narratives, tables, and charts. Provides white glove user onboarding and embedded, forward-deployed style support to help developers, QA, analytics teams, and business users operationalize GenAI-enabled reporting solutions in regulated enterprise environments.
- Business UAT & production readiness: Plan, execute, and document UAT test cases, expected results, evidence, defects, regression validation, acceptance criteria, requirements traceability, user sign-off, and release-readiness decisions.
- GenAI-enabled ingestion and reporting: Support developers and business teams in designing, testing, and validating dynamic data ingestion and report generation workflows, including source-to-report reconciliation and business-ready outputs.
- GenAI output validation: Validate extracted data, nested JSON payloads, generated summaries, trend insights, anomaly detection outputs, and narrative, table, and chart results using human-in-the-loop review and business-rule checks.
- JSON/API and data quality validation: Validate REST API request/response payloads, nested JSON, schema alignment, metadata completeness, SQL reconciliation, source-to-output accuracy, and Jira-supported defect resolution using Postman and Swagger/OpenAPI.
- Forward-deployed stakeholder support: Work closely with business users, product owners, developers, QA, model risk, validation, analytics, and technology teams to clarify requirements, resolve rollout issues, and close feedback loops during delivery.
- White glove onboarding and adoption: Create onboarding guides, SOPs, user guides, training materials, UAT artifacts, knowledge-transfer content, and adoption playbooks; facilitate walkthroughs, answer user questions, capture feedback, and coordinate early-life support.
Technical Skills
GenAI Skills: Prompt-assisted extraction, field/entity mapping, GenAI output validation, report-ready JSON generation, summarization, trend analysis support, anomaly detection review, exception handling, human-in-the-loop quality checks, and validation of generated narratives, tables, and charts.
SQL / Databases: Strong SQL for complex analytical queries, source-to-target validation, reconciliation, semi-structured data analysis, data quality checks, production-readiness testing, relational database concepts, and use of SQL workbench/query tools for testing and validation.
Python: pandas, NumPy, JSON parsing/transformation, dynamic ingestion support, report generation workflows, analytics automation, and pipeline testing.
JSON / APIs: REST APIs, request/response payloads, nested JSON, Postman, Swagger/OpenAPI, schema checks, metadata validation, and API testing.
Tools / Methods: Jira, Agile/Scrum methodologies, AWS cloud platforms, dashboards, visualization, model monitoring, documentation tools, developer collaboration, white glove onboarding, and forward-deployed enablement.
Experience & Qualifications
Experience: 7 years of software development, analytics, data engineering, monitoring, visualization, production support, dynamic ingestion support, report-generation testing, and business enablement experience.
Business UAT: 5 years validating requirements, test cases, defects, fixes, regression outcomes, generated reports, data outputs, user adoption needs, and production readiness with stakeholders and developers.
Regulated delivery: Financial services or regulated enterprise experience, including documentation, validation, model risk, analytics, governance reporting, stakeholder engagement, and enterprise delivery standards.
Education
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering or a related quantitative field.