Senior Data Engineer

Overview

Requirements

• Key Responsibilities* Collaborate with Analytics Delivery Leads and Lead Data Engineers to understand business requirements and deliver impactful data solutions.
• Work closely with Data Scientists and cross-functional teams to solve complex business problems through data engineering solutions.
• Manage data onboarding, data access, and stakeholder coordination for analytics initiatives.
• Design, develop, and maintain scalable, secure, and high-performance data pipelines.
• Acquire, ingest, process, and transform large-scale structured and unstructured datasets.
• Implement data engineering best practices for building reliable and production-ready data platforms.
• Perform data wrangling, cleansing, transformation, and feature engineering for machine learning and analytics use cases.
• Design and develop modular data pipelines to generate reusable features and modeling datasets.
• Build and optimize data architectures supporting advanced analytics and machine learning workloads.
• Contribute to data platform design by selecting appropriate technologies across Big Data, SQL, and NoSQL ecosystems.
• Ensure data quality, integrity, governance, security, and scalability across the data lifecycle.
• Participate in Agile squads and collaborate effectively with stakeholders across business and technology teams.
• Contribute to enterprise data architecture strategy and roadmap aligned with business objectives.
• Required Technical SkillsCore Data Engineering* Strong experience in Data Engineering and Big Data solutions.
• Expert-level proficiency in PySpark.
• Strong programming experience in Python.
• Experience building large-scale distributed data processing pipelines.

Data Processing & ETL* Data Ingestion

• Data Transformation
• Data Wrangling
• Data Preparation
• Data Modeling
• Feature Engineering
• ETL/ELT Development
• Batch Processing

Database Technologies* Strong SQL expertise

• Experience with Relational Databases
• Experience with NoSQL Databases

• Data Warehousing Concepts

• Google Cloud Data Services

• Databricks
• Hadoop Ecosystem
• Machine Learning Data Support* Feature Engineering for ML Models
• Data Preparation for Analytics and AI Use Cases
• Building Modeling and Feature Tables

• Data Quality Management

• Performance Tuning

• Version Control (Git)

• CI/CD Concepts
• Documentation Standards
• Required Soft Skills* Strong stakeholder management and communication skills.
• Experience working with Business Analysts, Data Scientists, and Product Teams.
• Ability to translate business requirements into scalable data solutions.
• Strong analytical and problem-solving skills.
• Experience working in Agile/Scrum environments.
• Ability to work independently and within cross-functional teams.
• Preferred Qualifications* Experience working in enterprise-scale data platforms.
• Experience supporting machine learning and advanced analytics initiatives.
• Exposure to cloud-native data engineering solutions.
• Experience designing modern data architectures and data lake environments.