Own and manage day-to-day operations of Data & Analytics platforms, ensuring SLA-driven support and service delivery
Support cross-functional teams by delivering data insights for complex business problems
Collaborate with data engineers, data scientists, and stakeholders to deliver data-driven outcomes
Gather requirements, define data models, and support solution design with architects and engineering teams
Perform data exploration, analysis, and maintain data definitions, business rules, and lineage
Drive documentation, testing (functional, regression, exploratory), and execution of test plans
Build, enhance, and support scalable data pipelines (batch and real-time) across multiple systems
Implement data governance practices and promote data literacy across the organization
Identify and address technical debt; continuously improve and optimize processes
Monitor, troubleshoot, and tune performance of data platforms and analytical solutions
Champion automation-first, enterprise-first, and reusable solution design principles
Support infrastructure and cloud-based solutions including SaaS, PaaS, IaaS, and IaC practices
Communicate insights and results through reporting and presentations

Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, or a related field
7+ years of experience in data analytics or data engineering
At least 2 years of experience in a leadership or mentorship role
Strong experience with modern data platforms including Databricks, Snowflake, and Azure Data Factory
Proficiency in SQL, Python, and/or R for data analysis and engineering
Experience with reporting and visualization tools such as Power BI
Knowledge of legacy and enterprise systems such as SAP, SQL Server, DataStage, and Unix scripting
Strong understanding of data engineering, analytics, and large-scale data processing
Experience with distributed data processing tools (e.g., PySpark)
Familiarity with tools like Splunk, Alation, and data governance frameworks

Lead Data Engineer

Key skills