Lead Data Engineer - job 1 of 2
- Design and build scalable data pipelines using Python (PySpark, Pandas, Airflow).
- Architect data lakes / warehouses (Snowflake, Redshift, or similar).
- Implement real-time and batch data processing frameworks.
- Optimize data models for analytics, reporting, and ML use cases.
- Work with datasets across:
- Portfolio & asset management
- Trade lifecycle & transaction data
- Client onboarding & KYC
- Performance reporting & attribution
- Build systems supporting investment analytics, risk, and regulatory reporting.
- Lead a team of data engineers; provide technical guidance and code reviews.
- Define data engineering best practices, governance, and standards.
- Collaborate with Product, Analytics, and Business teams.
- Drive data quality, lineage, and observability frameworks.
- Develop solutions on AWS:
- S3, Glue, Lambda, EMR, Redshift
- Implement CI/CD pipelines and infrastructure-as-code (Terraform/CloudFormation).
- Ensure security and compliance (FINRA, SEC considerations).
- 10+ years of experience in data engineering / data platform development
- 3+ years in a lead or architect role
- Strong programming expertise in Python
- Hands-on experience with:
- PySpark / Spark
- SQL & data modeling
- Workflow orchestration (Airflow, Prefect)
- Experience in wealth management / asset management / financial services
- Strong understanding of:
- Investment data models
- Market data & financial instruments
- Experience with cloud-native data platforms (AWS)
- Experience with Snowflake / Databricks
- Knowledge of machine learning pipelines
- Familiarity with data governance tools (Collibra, Alation)
- Exposure to real-time streaming (Kafka, Kinesis)
- Certifications in AWS or Data Engineering
- Python (Advanced)
- Data Architecture & Modeling
- Financial / Wealth Management Domain
- Cloud Data Engineering (AWS)
- Leadership & Stakeholder Management
- ETL / ELT Pipelines
- Big Data Technologies
- Scalable, high-performance data pipelines supporting critical investment workflows
- High data quality, governance, and compliance adherence
- Strong collaboration across business and technology teams
- Mentored and high-performing data engineering team
- Work on mission-critical financial data platforms
- High visibility with business and executive stakeholders
- Opportunity to shape next-gen data architecture in wealth management
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