Data Analyst/Engineer

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/Data Scientist 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

Technical Skills:

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.

Behavioral Skills:

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.