Associate Data Architect

Overview

Role: Data Analyst/ Engineer

Job Summary

Senior Level Data Engineer / Data Analyst technical lead with data analytics experience, Databricks, Pyspark and Python

This is a key role that requires senior/lead with great communication skills who is very proactive with risk & issue management.

Experience And Education Required

10+ years of experience as Data Analyst / Data Engineer with Databricks on AWS expertise in designing and implementing scalable, secure, and cost-efficient data solutions on AWS

Job Profile

• Hands-on data analytics experience with Databricks on AWS, Pyspark and Python
• Must have prior experience with migrating a data asset to the cloud using a GenAI automation option
• Experience in migrating data from on-premises to AWS
• Expertise in developing data models, delivering data-driven insights for business solutions
• Experience in pretraining, fine-tuning, augmenting and optimizing large language models (LLMs)
• Experience in Designing and implementing database solutions, developing PySpark applications to extract, transform, and aggregate data, generating insights
• Data Collection & Integration: Identify, gather, and consolidate data from diverse sources, including internal databases and spreadsheets ensuring data integrity and relevance.
• Data Cleaning & Transformation: Apply thorough data quality checks, cleaning processes, and transformations using Python (Pandas) and SQL to prepare datasets.
• Automation & Scalability: Develop and maintain scripts that automate repetitive data preparation tasks.
• Autonomy & Proactivity: Operate with minimal supervision, demonstrating initiative in problem-solving, prioritizing tasks, and continuously improving the quality and impact of your work
  
  

• Minimum of 10 years of experience as a Data Analyst, Data Engineer, or related role, ideally with a bachelor’s degree or higher in a relevant field.
• Strong proficiency in Python (Pandas, Scikit-learn, Matplotlib) and SQL, with experience working across various data formats and sources.
• Proven ability to automate data workflows, implement code-based best practices, and maintain documentation to ensure reproducibility and scalability.
  
  

• Ability to manage in tight circumstances, very pro-active with risk & issue management
• Requirement Clarification & Communication: Interact directly with colleagues to clarify objectives, challenge assumptions.
• Documentation & Best Practices: Maintain clear, concise documentation of data workflows, coding standards, and analytical methodologies to support knowledge transfer and scalability.
• Collaboration & Stakeholder Engagement: Work closely with colleagues who provide data, raising questions about data validity, sharing insights, and co-creating solutions that address evolving needs.
• Excellent communication skills for engaging with colleagues, clarifying requirements, and conveying analytical results in a meaningful, non-technical manner.
• Demonstrated critical thinking skills, including the willingness to question assumptions, evaluate data quality, and recommend alternative approaches when necessary.
• A self-directed, resourceful problem-solver who collaborates well with others while confidently managing tasks and priorities independently.
  
  

Mphasis applies next-generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis’ Front2Back™ Transformation approach. Front2Back™ uses the exponential power of cloud and cognitive to provide hyper-personalized (C=X2C2TM=1) digital experience to clients and their end customers. Mphasis’ Service Transformation approach helps ‘shrink the core’ through the application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world. Mphasis’ core reference architectures and tools, speed and innovation with domain expertise and specialization are key to building strong relationships with marquee clients