Data Engineer

Location: Plano, TX Salary: $70.00 USD Hourly - $75.00 USD Hourly Description: Our client is currently seeking a Data Engineer with strong experience in data modeling, data warehousing, and cloud-based data platforms. The ideal candidate will have hands-on experience with AWS services and a strong foundation in SQL and big data technologies. You will play a key role in building and optimizing scalable data pipelines and ensuring data quality across systems.
Required Qualifications

• 5+ years of experience in data modeling, data warehousing, data analysis, and data profiling
• Strong ability to identify trends, patterns, and anomalies in large datasets
• Hands-on experience with AWS services, including:
• S3
• AWS Glue
• EMR
• IAM roles and permissions
• Proficiency in one or more query/programming languages:
• SQL, PL/SQL, DDL
• SparkSQL, Scala
• Experience working with relational databases (e.g., Teradata)
• Experience handling both structured and unstructured data

Preferred Qualifications

• Strong programming skills in SQL and Python
• Experience with NoSQL / non-relational databases such as:
• Document stores
• Key-value databases
• Graph or column-family databases
• Hands-on experience with Snowflake and Databricks

Nice to Have

• Experience with visualization tools such as Tableau or Power BI
• Understanding of financial services industry data and business processes
• AWS certifications (e.g., Cloud Practitioner, Developer Associate)

Education & Additional Skills

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field (or equivalent experience)
• Experience with scripting languages such as Python or Shell (KornShell/Bash)

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL processes
• Perform data profiling and quality checks to ensure data integrity
• Work with large datasets in both batch and distributed processing environments
• Collaborate with cross-functional teams to gather and deliver data requirements
• Optimize data storage, processing, and querying for performance efficiency

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