Senior Data Engineer
Summary
Designs and builds scalable Azure-based data pipelines and architectures using PySpark, Databricks, and Synapse to power analytics and real-time insights.
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, Communicating trade‑offs to architects and product teams
Experience working with Azure DevOps or GitHub Actions
Nice to Have Skills: Experience with Streaming & Real-Time Data Processing on Designing low‑latency pipelines.
Exposure to Data Governance & Metadata Management in Data classification & sensitivity labels
Familiarity with Advanced Python Engineering Practices on Performance‑aware Python design
Experience working in Analytics & BI Awareness
Knowledge of Cloud Cost Optimization & FinOps
Detailed Job Description The Senior Data Engineer will be responsible for designing, building, and operating scalable, secure, and high‑performance data platforms. The role involves critical data pipelines and architectures that power analytics, reporting, and advanced data use cases such as machine learning and real‑time insights.
The candidate will collaborate closely with analytics, data science, product, and business teams while mentoring engineers and driving engineering best practices across the data organization.
The role combines technical leadership, architecture ownership, and business impact.
Minimum Years of Experience : 6 years
Certifications Needed: No
Top 3 responsibilities you would expect the Subcon to shoulder and execute
- Design & Own Scalable Data Architecture: Design batch and streaming data architectures, Choose the right storage, compute, and processing patterns, Ensure scalability, reliability, fault tolerance, and cost efficiency.
- Build Reliable, High‑Quality Data Pipelines: Develop and maintain ETL/ELT pipelines at scale, Handle incremental loads, CDC, and failure recovery, Implement data quality checks and monitoring.
- Technical Leadership & Collaboration: Review code, data models, and designs, Translate business requirements into technical solutions, Communicate trade‑offs to stakeholders and leadership