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About the Role

The Senior Data Engineer will design, build, and operate scalable, secure, and high-performance data platforms. This role involves critical data pipelines and architectures powering analytics, reporting, and advanced data use cases like machine learning and real-time insights. The candidate will collaborate with analytics, data science, product, and business teams, mentor engineers, and drive engineering best practices.

Responsibilities

Design and own scalable data architecture, including batch and streaming patterns.
Choose appropriate storage, compute, and processing patterns.
Ensure scalability, reliability, fault tolerance, and cost efficiency of data platforms.
Develop and maintain ETL/ELT pipelines at scale.
Handle incremental loads, CDC, and failure recovery.
Implement data quality checks and monitoring.
Review code, data models, and designs.
Translate business requirements into technical solutions.
Communicate trade-offs to stakeholders and leadership.

Requirements

Minimum 6 years of experience.
Strong hands-on experience with Azure Data Lake Gen2, Synapse, Databricks.
Solid experience in PySpark / Spark SQL, Databricks performance tuning & job optimization.
Proficient in Complex SQL (window functions, CTEs, execution plans).
Strong understanding in mentoring junior and mid-level engineers.
Experience communicating trade-offs to architects and product teams.
Experience working with Azure DevOps or GitHub Actions.

Skills

Azure Data Lake Gen2
Synapse
Databricks
PySpark
Spark SQL
Databricks performance tuning
Job optimization
Complex SQL
Window functions
CTEs
Execution plans
Mentoring
Azure DevOps
GitHub Actions
Streaming
Real-Time Data Processing
Low-latency pipelines
Data Governance
Metadata Management
Data classification
Sensitivity labels
Advanced Python Engineering Practices
Performance-aware Python design
Analytics
BI Awareness
Cloud Cost Optimization
FinOps

Experience Level

6 years