Senior Data Engineer

Analytics Data Engineer – Treasury/Finance

Duration: 6 months

Extension Potential: No

FTE Conversion: No

Work Hours: 9:00 AM – 5:00 PM

Location: Remote or Hybrid (must be able to visit the office for technical issues or special occasions)

Key Responsibilities

• Data Collection & Preparation: Gather data from multiple sources, clean and preprocess to ensure accuracy and consistency.
• Statistical Analysis: Apply statistical modeling techniques (hypothesis testing, regression, clustering) to identify patterns, trends, and relationships.
• Programming & Analysis: Use Python, SQL, or R to analyze data and uncover insights that address business challenges such as customer behavior, operational efficiency, and cost optimization.
• Visualization & Reporting: Create dashboards and visualizations using Tableau, Power BI, or matplotlib to simplify complex data for stakeholders and support data-driven decisions.
• Data Engineering & ETL: Develop robust ETL pipelines to handle large datasets from IBM Netezza/Hadoop and other sources, ensuring efficient processing and transformation.
• Advanced Analytics: Incorporate predictive modeling and machine learning techniques to solve complex business problems.
• Collaboration: Work with business teams to translate requirements into scalable, actionable data solutions.

Must-Have Skills

Programming & Tools

• 8-10 years of strong experience in Python, SAS, and SQL
• Power BI, DAX, and M Code for dashboard development
• Proficiency in MS 365 Suite: Office, Power Automate, SharePoint, OneDrive
• Advanced SQL Server configuration for high-throughput analytics (memory allocation, parallel query execution)
• Experience with partitioned tables, indexed views, and columnstore indexes
• Deep understanding of SQL Server recovery models, backup/restore strategies, and disaster recovery planning
• Design and implementation of ETL pipelines with incremental loads and change data capture
• Data staging and processing large-scale datasets efficiently
• Experience with star/snowflake schemas, fact/dimension modeling, and slowly changing dimensions
• Batch processing pipelines using Python and SQL
• Knowledge of RDBMS, NoSQL, and data file formats (CSV, Parquet, JSON)
• Translate business requirements into scalable, optimized data models
• Strong experience with AWS (Redshift, Glue, MLOps)

Nice-to-Have Skills

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