Data Engineer
- Steneral
2 Days Ago
NA
W2
Dallas-TX
4-10 Years
Required Skills: Python, Spark, Hadoop, ETL, Data Warehousing, Cloud Platforms, Streaming, Governance
Job Description
Position Overview :
We are seeking a highly skilled Data Engineer / Data Analyst to support enterprise data and analytics initiatives focused on monitoring market, liquidity, credit, and operational risk. This is a hands-on role requiring strong technical expertise, excellent communication skills, and the ability to work onsite in Downtown Dallas.
Key Responsibilities
- Data analysis: Work with large datasets to identify trends, anomalies, exposures, and risk indicators.
- Data modeling: Design databases and models using Microsoft Fabric and related technologies.
- Pipeline development: Build, maintain, and optimize ETL/ELT pipelines across multiple sources.
- Automation: Perform data manipulation, validation, and transformation using SQL, Python, Excel, and VBA.
- Dashboarding: Develop dashboards and automated reporting using Tableau and BI tools.
- Risk analytics: Analyze market, liquidity, credit, counterparty, and operational risk data.
- Collaboration: Partner with Risk SMEs, business stakeholders, and technology teams.
- Documentation: Document data sources, business rules, workflows, pipelines, and transformation logic.
Required Skills & Qualifications
- Bachelor’s degree (mandatory).
- Strong hands-on experience with SQL, Python, BI, Tableau, Excel/VBA, ETL/ELT pipelines.
- Experience working with large datasets (cleansing, validation, transformation, analysis).
- Expertise in database design and analytical solutions.
- Microsoft Fabric experience (Data Factory, Lakehouse, Warehouse).
- Excellent communication skills and ability to work onsite full-time.
- Background in financial services, banking, investment, trading, or risk management preferred.
Why Join
- Opportunity to work on cutting-edge risk analytics in financial services.
- Exposure to Microsoft Fabric and advanced BI tools.
- Collaborative environment with risk SMEs and technology teams.
- Professional growth through complex enterprise data initiatives.
