Senior Data Engineer

We are seeking a seasoned Senior Data Engineer with 12+ years of experience in designing, building, and managing scalable data pipelines and architectures. The role requires deep expertise in modern data engineering practices, cloud platforms, and enterprise-grade data solutions to support analytics, reporting, and strategic decision-making.

• ETL/ELT Development : Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data from diverse sources.

• Data Lake & Warehouse Design : Architect and implement scalable data lakes and warehouses to support analytics and BI workloads.

• System Optimization : Optimize data processing frameworks for performance, scalability, and cost efficiency.

• Data Security : Implement security best practices, encryption, and role-based access controls.

• Data Governance : Ensure compliance with governance frameworks, data lineage, and quality standards.

• Collaboration : Work closely with data scientists, analysts, and business stakeholders to deliver reliable data solutions.

• Automation & CI/CD : Integrate DevOps practices for automated deployment and monitoring of data pipelines.

• Mentorship : Provide technical guidance and mentorship to junior engineers, fostering a culture of excellence.

• Programming Expertise : Advanced proficiency in Python, Scala, and/or Java for data engineering tasks.

• Cloud Platforms : Hands-on experience with AWS, Azure, or GCP for building cloud-native data solutions.

• Data Architecture : Strong knowledge of data modeling, warehousing, and distributed systems.

• Big Data Tools : Experience with Spark, Hadoop, Kafka, or equivalent frameworks.

• SQL Mastery : Expertise in writing optimized queries, stored procedures, and performance tuning.

• Visualization Support : Ability to prepare curated datasets for BI tools like Power BI or Tableau.

• Problem-Solving : Strong analytical and troubleshooting skills for complex data challenges.
  

• Experience with containerization (Docker, Kubernetes) for data workloads.

• Knowledge of real-time streaming architectures (Kafka, Kinesis, Event Hub).

• Familiarity with data mesh and lakehouse architectures .

• Exposure to machine learning pipelines and advanced analytics integration.

• Proven track record of leading enterprise-scale data engineering projects .