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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