Senior Data Engineer

Role Overview

• As a Data Engineer within the Data Foundation team, you will act as a technical subject-matter expert responsible for the configuration, development, integration, and operation of enterprise data platforms and products.
• You will collaborate closely with Product Owners, Product Architects, Engineering Managers, and cross-functional teams to translate business requirements into robust technical solutions aligned with engineering standards and roadmaps. The role involves hands-on development, agile delivery, documentation, and continuous improvement across the full data lifecycle.
• This position reports to the Data Engineering Lead.

Key Responsibilities

• Design, develop, and maintain scalable and reliable data pipelines and ETL/ELT processes
• Act as an individual contributor (~60%), delivering high-quality data engineering solutions
• Coach and mentor data engineers on best practices, design patterns, and implementation approaches
• Translate high-level architecture and design documents into executable development tasks
• Optimize data infrastructure performance and resolve complex technical issues and bottlenecks
• Drive technical excellence through code reviews, design reviews, testing, and deployment practices
• Ensure adherence to coding standards, architectural guidelines, and security best practices
• Embed DevSecOps and CI/CD practices into daily engineering workflows
• Manage and reduce technical debt while guiding sustainable development approaches
• Lead and contribute to cross-functional technical discussions and solution design reviews
• Support hiring, onboarding, mentoring, and development of engineering talent
• Improve engineering processes to enhance delivery efficiency, quality, and reliability
• Collaborate with Product Owners, Engineering Managers, Business Analysts, and Scrum Masters to align on sprint goals, timelines, and priorities

Technology Stack

Must Have

• Strong proficiency in Python and SQL
• Experience with big data technologies, including MPP systems and streaming platforms
• Hands-on experience with cloud platforms (AWS, Azure, or GCP)
• Experience with data and compute platforms such as Databricks, Snowflake, or BigQuery
• Experience with CI/CD tools (Azure DevOps, Jenkins, Git)
• Experience integrating data via APIs
• Ability to interpret architecture and design documentation

Nice to Have

• Experience with Microsoft data stack (Azure Data Factory, Azure Synapse, Databricks, Fabric, Power BI)
• Data modeling and data architecture expertise
• ETL pipeline design and performance optimization
• Advanced PySpark expertise
• Logging and monitoring using Azure or Databricks services
• Experience with Apache Kafka or similar streaming technologies
• Exposure to machine learning and AI technologies

Qualifications and Experience

• 3–5 years of professional experience in Data Engineering
• Strong hands-on experience with data integration, ETL/ELT, and data warehousing
• Solid understanding of software engineering principles, coding standards, and modern architectures
• Experience delivering end-to-end DataOps / Data Engineering projects
• Familiarity with data governance, security, and compliance standards
• Proven ability to mentor and guide engineers across experience levels
• Strong analytical, problem-solving, and decision-making skills
• Ability to work independently while collaborating effectively in team environments
• Excellent written and verbal communication skills
• Pragmatic mindset with a strong focus on quality and delivery