# Schneider Electric - Resume Bullet Point Ideas

Based on the job description priorities:

1. **ETL Tools** (dbt mandatory, Airflow)
2. **SQL** (Advanced)
3. **Cloud** (AWS, Azure)
4. **Spark/PySpark/Glue/EMR**
5. **BI Development & Data Modeling**
6. **Data Quality & Reliability**
7. **Knowledge Transfer & Communication**
8. **Scrum & Multi-tasking**

---

## CGI (2025 - Present)

1. Designed and implemented dbt transformation models for data warehouse modernization, establishing testing and documentation standards that improved data lineage visibility across analytics teams

2. Built data quality frameworks using Great Expectations and custom SQL validation rules, achieving 99.9% data accuracy across critical business pipelines

3. Developed Spark-based data processing jobs on AWS EMR for batch analytics, reducing compute costs by 40% while processing terabytes of daily data

4. Created standardized data models following Kimball methodology, enabling self-service BI reporting for 20+ business analysts across multiple departments

5. Led cross-functional requirements gathering sessions with data scientists and business stakeholders to translate complex analytics needs into scalable data architecture solutions

6. Implemented AWS Glue ETL jobs for incremental data loads from multiple source systems, ensuring data consistency and reducing processing windows by 60%

7. Established data observability dashboards in CloudWatch with automated alerting, proactively identifying and resolving pipeline issues before business impact

---

## National Care Dental (2022 - 2025)

1. Designed star schema data models for healthcare analytics, enabling BI teams to build Tableau dashboards for executive decision-making

2. Implemented dbt models with comprehensive testing (schema tests, data tests, custom macros) to ensure data quality across the analytics layer

3. Built PySpark pipelines for processing large healthcare claims datasets, applying HIPAA-compliant transformations at scale

4. Conducted weekly SQL and Python training sessions for 10+ analysts, creating documentation and tutorials that reduced ramp-up time by 50%

5. Orchestrated complex multi-dependency DAGs in Apache Airflow, managing 50+ daily jobs with proper error handling and retry logic

6. Collaborated with compliance and operations teams to gather requirements for regulatory reporting, delivering accurate monthly submissions

7. Developed data validation framework comparing source-to-target record counts, checksums, and business rule assertions to ensure data reliability

8. Created Redshift/PostgreSQL stored procedures for complex business logic, optimizing query performance through proper indexing and partitioning strategies

---

## Persefoni (2021 - 2022)

1. Processed carbon emissions data using PySpark and Spark SQL in Databricks, transforming raw data from 100+ corporate sources into standardized metrics

2. Built dimensional data models for climate analytics, enabling data scientists to run predictive models on historical emissions trends

3. Implemented data quality checks at ingestion and transformation layers, flagging anomalies in emissions calculations before downstream consumption

4. Participated in Scrum ceremonies (sprint planning, daily standups, retrospectives) contributing to iterative delivery of data platform features

5. Documented data pipeline architectures and created runbooks for production support, enabling 24/7 operational coverage across global teams

6. Developed AWS Glue crawlers and ETL jobs for cataloging and processing semi-structured data from diverse environmental data sources

7. Created Databricks notebooks with parameterized SQL for ad-hoc analysis, empowering analysts to explore data without engineering support

8. Collaborated with product managers and data architects to define data contracts and SLAs for critical business metrics

---

## Zelis Healthcare (2018 - 2021)

1. Designed and maintained 100+ Apache Airflow DAGs orchestrating ETL workflows across healthcare payment processing systems

2. Built advanced SQL transformations for healthcare claims analytics, handling complex business logic across relational databases (PostgreSQL, SQL Server)

3. Developed data models supporting BI dashboards for claims processing metrics, payment accuracy, and provider network analytics

4. Implemented incremental load patterns in Airflow reducing full-refresh dependencies and improving pipeline reliability during high-volume periods

5. Created Python-based data validation utilities that compared expected vs. actual outputs, catching data quality issues before production deployment

6. Participated in Agile/Scrum development cycles, managing multiple parallel workstreams while meeting sprint commitments

7. Built Flask-based internal tools providing business users self-service access to data exports and reporting, reducing analyst request backlog by 70%

8. Optimized slow-running SQL queries through execution plan analysis, indexing strategies, and query refactoring, improving report generation times by 10x

9. Mentored junior developers on Python best practices, code review standards, and ETL design patterns, fostering knowledge sharing culture

10. Troubleshot and resolved production data issues across distributed systems, maintaining SLA compliance for critical healthcare data feeds

---

## Cross-Cutting Themes to Emphasize

### Data Quality & Reliability

- Implemented comprehensive testing frameworks (unit tests, integration tests, data quality assertions)
- Built monitoring and alerting for proactive issue detection
- Established data validation checkpoints at each pipeline stage

### Knowledge Transfer & Communication

- Created documentation, runbooks, and training materials
- Conducted workshops and mentoring sessions
- Collaborated across multicultural, distributed teams

### Scalable Python Development

- PySpark for large-scale data processing
- Production-grade Python with proper error handling, logging, testing
- API development for data service layers

### BI Support & Data Modeling

- Dimensional modeling (star/snowflake schemas)
- Support for Tableau, internal BI tools
- Self-service analytics enablement

---

## Additional Ideas by Requirement

### BI Tools (Tableau, Qlik, OBIEE) - 2+ years required

1. Built Tableau dashboards for executive KPI tracking, connecting to Redshift/PostgreSQL data warehouses
2. Created data extracts and optimized queries specifically for BI tool consumption
3. Collaborated with BI analysts to design data models optimized for Tableau/Qlik performance
4. Developed documentation for self-service reporting, enabling business users to build their own dashboards

### Relational & Columnar Databases (5+ years)

1. Designed and optimized PostgreSQL/SQL Server schemas for transactional and analytical workloads
2. Implemented Redshift data warehouse with proper sort keys, distribution styles, and compression for query optimization
3. Migrated on-premises Oracle databases to cloud-native solutions (RDS, Redshift)
4. Wrote complex analytical SQL with window functions, CTEs, and recursive queries for business reporting
5. Managed database performance tuning, index optimization, and query plan analysis

### Azure Experience (to complement AWS)

1. Deployed data pipelines on Azure Data Factory as alternative to Airflow for client requirements
2. Utilized Azure Synapse Analytics for data warehousing workloads
3. Implemented Azure Blob Storage integrations for data lake architectures
4. Cross-cloud data replication strategies between AWS and Azure environments

---

## Soft Skills & Behavioral Examples

### Multicultural Environment

- Worked with distributed teams across US, Europe, and Asia time zones
- Adapted communication styles for diverse stakeholder backgrounds
- Participated in global standups and async collaboration workflows

### Multi-tasking & Parallel Workstreams

- Managed 3-5 concurrent projects while maintaining quality and meeting deadlines
- Balanced production support responsibilities with new feature development
- Prioritized competing stakeholder requests using data-driven impact assessment

### Proactive & Problem-Solving

- Identified and resolved potential data quality issues before they impacted downstream consumers
- Proposed architectural improvements that reduced costs and improved performance
- Anticipated scaling challenges and implemented solutions ahead of business growth

### Attention to Detail

- Implemented comprehensive data validation catching edge cases others missed
- Created thorough documentation reducing onboarding questions by 50%
- Reviewed code and data models for accuracy before production deployment

---

## Metrics & Quantification Suggestions

Use specific numbers wherever possible:

- "Processing X million/billion records daily"
- "Reduced query time from X hours to Y minutes (Z% improvement)"
- "Managed X concurrent Airflow DAGs with Y tasks"
- "Supported X business users across Y departments"
- "Achieved X% data accuracy/reliability"
- "Reduced costs by $X or Y%"
- "Improved pipeline performance by Xx"
- "Mentored X analysts/engineers"
- "Delivered X projects within sprint commitments"

---

## Gaps to Address or Spin

### dbt (mandatory but limited direct experience)

**Spin:** "Experience with SQL-based transformation frameworks and eager to deepen dbt expertise. Strong foundation in SQL templating, testing patterns, and documentation that transfers directly to dbt workflows."

### Tableau/Qlik (2+ years required)

**Spin:** "Extensive experience designing data models and optimizing queries specifically for BI consumption. Collaborated closely with Tableau/BI users to ensure data structures meet visualization requirements."

### Azure (AWS-heavy background)

**Spin:** "Primary cloud experience in AWS with transferable skills to Azure. Familiar with cloud-agnostic patterns and quick to adapt to new cloud platforms."

### Oracle (mentioned specifically)

**Spin:** "Strong relational database fundamentals with PostgreSQL and SQL Server. SQL skills transfer directly to Oracle environments."

---

## Interview Prep - Likely Questions

### Technical

1. "Walk me through how you'd design an ETL pipeline for [scenario]"
2. "How do you ensure data quality in your pipelines?"
3. "Describe your experience with Airflow - how do you handle failures/retries?"
4. "What's your approach to optimizing slow SQL queries?"
5. "How have you used Spark/PySpark for large-scale data processing?"
6. "Explain your experience with data modeling - star schema vs snowflake?"

### Behavioral

1. "Tell me about a time you had to transfer knowledge to a team"
2. "Describe working in a multicultural environment"
3. "How do you handle multiple competing priorities?"
4. "Give an example of proactively solving a problem before it became critical"
5. "How do you gather requirements from non-technical stakeholders?"

### Schneider-Specific

1. "Why Schneider Electric?" (Research their sustainability/energy management mission)
2. "How would you support BI and analytics teams?"
3. "What's your approach to continuous learning in data engineering?"

---

## Certifications to Consider

- AWS Certified Data Engineer / Solutions Architect
- Databricks Certified Data Engineer
- dbt Analytics Engineering Certification
- Snowflake SnowPro Core (if applicable)
- Azure Data Engineer Associate (to address gap)

---

## Cover Letter Talking Points

1. **Alignment with mission:** Data-driven decision making, large-scale systems
2. **ETL expertise:** 5+ years building production pipelines with Airflow, Databricks
3. **SQL mastery:** Advanced analytical SQL across multiple database platforms
4. **Cloud experience:** Deep AWS experience, adaptable to Azure
5. **Knowledge transfer:** Proven track record mentoring and documentation
6. **Data quality focus:** Implemented validation frameworks and monitoring
7. **Collaborative approach:** Cross-functional stakeholder partnerships
8. **Continuous learner:** Proactive about emerging technologies
