- Company Name
- Fearless
- Job Title
- Senior Data Engineer (Senior Consultant, Data & Analytics)
- Job Description
-
**Job Title**
Senior Data Engineer (Senior Consultant, Data & Analytics)
**Role Summary**
Design, build, and maintain a secure, scalable data platform in AWS to monitor and protect the Paid Family and Medical Leave (PFML) fund. Lead end‑to‑end data pipelines, develop fraud‑detection models, and uphold data integrity, confidentiality, and compliance while mentoring cross‑functional teams.
**Expectations**
- Senior‑level independent contributor with advanced data engineering expertise.
- Architect scalable pipelines, advanced analytics, and defensive data quality checks.
- Collaborate with executives and stakeholders to turn data into mission‑driven insights.
- Mentor peers and prove technical excellence, ensuring timely delivery aligned with client priorities.
**Key Responsibilities**
1. Build and manage ETL/ELT pipelines using AWS Glue (Python/Spark) and SQL to ingest Salesforce APIs, Ruby on Rails (PostgreSQL), and external sources into an AWS environment.
2. Develop fraud‑detection logic and data models to flag high‑risk patterns (identity theft, duplicate claims, suspicious banking changes).
3. Conduct forensic data analysis with SQL/Python to uncover claim anomalies (IP clusters, disposable emails, wage inconsistencies).
4. Create automated monitoring alerts in AWS Redshift or Athena to detect improper payments pre‑processing.
5. Implement versioning, logging, and immutable data lake practices for traceability during investigations.
6. Enforce regulatory compliance: data masking, encryption of PII/PHI to meet HIPAA and state privacy laws.
7. Lead architecture reviews, capacity planning, and performance tuning across large‑scale cloud deployments.
8. Guide data literacy and analytics adoption within client teams.
**Required Skills**
- **AWS Stack:** S3, Redshift, Glue, Lambda, Step Functions, Athena.
- **Databases:** PostgreSQL, relational schema design, Salesforce data structures.
- **Programming:** Python (data manipulation, validation libraries), advanced SQL (window functions, CTEs, performance tuning).
- **ETL/ELT & Orchestration:** Glue, dbt, Airflow, Step Functions.
- **Data Modeling:** Dimensional modeling (Star/Snowflake), Data Vault 2.0.
- **Analytics & Statistics:** Outlier detection, trend analysis on large datasets.
- **Security & Compliance:** Data masking, encryption, HIPAA/PII protection.
- **Quality & Integrity:** Defensive pipeline design, automated data quality checks, audit trails.
- **Problem Solving:** Troubleshoot discrepancies between source (Rails) and reporting layers.
- **Communication:** Translate technical findings to executives and stakeholders; mentor peers in data practices.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related field.
- Optional certifications: AWS Certified Solutions Architect, AWS Data Analytics Specialty, or equivalent data engineering credentials.