Data Engineer with data science exp

Role : Data Engineer with data science exp
Location : Scottsdale AZ (Onsite)
Rate : $75/hr.
Indent :
SF_OP_201150-1-1

We are looking for a skilled Data Engineer with strong PySpark experience to work on large-scale data processing and analytics initiatives. The ideal candidate will have hands-on experience working with large datasets, complex joins, and performance optimization, along with the ability to apply basic analytical thinking and deliver clear, stakeholder-ready outputs.

Key Responsibilities
Data Engineering & Development

• Design, develop, and maintain scalable data pipelines using PySpark.
• Write efficient and optimized PySpark code to process and transform large-scale datasets.
• Handle joins across multiple large databases, ensuring performance, accuracy, and scalability.
• Optimize Spark jobs to minimize runtime, memory usage, and compute cost.
• Work with structured and semi-structured data from multiple sources.

Data Preparation & Analysis Support

• Build and curate training and analytical datasets by joining and transforming multiple data sources.
• Apply basic analytical skills to understand data patterns, anomalies, and business relevance.
• Perform data validation and quality checks, including:
  • Record counts and reconciliation
  • Duplicate detection
  • Null and outlier checks
  • Schema and data-type validation
• Ensure datasets are analysis-ready and trustworthy.

Stakeholder Interaction & Reporting

• Understand business objectives and translate them into data requirements.
• Ask the right questions to determine:
  • Level of aggregation required
  • Metrics definitions
  • Data freshness and accuracy expectations
  • Preferred output and reporting formats
• Present results and insights clearly to stakeholders.
• Create reports and summaries using Excel for business users and leadership.

Expected Technical Approach (Problem-Solving Mindset)
Candidates are expected to demonstrate the ability to:

• Approach complex data projects methodically, starting with:
  • Understanding business objectives
  • Reviewing source data structure and volume
  • Designing efficient join strategies
• Choose the right join types, partitioning strategies, and caching techniques.
• Validate data at every stage of the pipeline.
• Balance technical accuracy with business usability when presenting results.

Core Skill Sets (Must-Have)

• Strong hands-on experience with PySpark
• Extensive experience working with large datasets
• Proven expertise in joining large databases efficiently
• Ability to write high-performance, optimized code
• Basic analytical skills to interpret and validate data
• Reporting skills using Excel

Good to Have Skills

• Experience in model development or supporting analytics/modeling teams
• SAS experience
• Exposure to Cloudera or similar big data platforms
• Understanding of data warehousing and analytics workflows

Soft Skills & Competencies

• Strong problem-solving and logical thinking

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