Senior Data Engineer

Data Engineer – 10+ Years Experience

Job Title: Senior Data Engineer
Experience: 10+ Years

Job Summary

We are looking for a highly experienced Senior Data Engineer with 10+ years of experience designing, developing, and maintaining scalable data platforms, ETL/ELT pipelines, data warehouses, and cloud-based data solutions.

Required Skills

• 10+ years of experience in Data Engineering / Data Warehousing
• Strong expertise in Python, SQL, and PySpark
• Hands-on experience with AWS / Azure / Google Cloud Platform cloud platforms
• Strong experience building ETL/ELT pipelines
• Experience with AWS Glue, EMR, Lambda, S3, Redshift or equivalent cloud services
• Strong knowledge of Apache Spark / PySpark
• Experience with Snowflake, Databricks, Redshift or other modern data platforms
• Expertise in data modeling, dimensional modeling, and data warehousing concepts
• Experience with Kafka / streaming data / real-time pipelines
• Strong understanding of data lake and data lakehouse architectures
• Experience with Airflow or other workflow orchestration tools
• Strong SQL skills, including query optimization and performance tuning
• Experience with CI/CD, Git, Jenkins/GitHub Actions, and DevOps practices
• Knowledge of Docker and Kubernetes is a plus
• Experience working with large-scale datasets and distributed computing
• Strong understanding of data quality, governance, security, and data lineage
• Experience working in Agile/Scrum environments

Preferred Skills

• AWS Certified Data Analytics / Solutions Architect
• Databricks experience
• Snowflake experience
• Terraform / Infrastructure as Code
• Kafka and event-driven architecture
• Machine learning data pipelines
• Experience leading technical teams and mentoring junior data engineers

Key Responsibilities

• Design and develop scalable batch and real-time data pipelines.
• Build and optimize ETL/ELT processes using Python, SQL, and PySpark.
• Develop cloud-based data lake, warehouse, and lakehouse solutions.
• Integrate data from multiple sources and ensure data quality and reliability.
• Optimize Spark jobs, SQL queries, and data processing workflows.
• Implement data governance, security, and monitoring standards.
• Collaborate with architects, analysts, data scientists, and business teams.
• Lead technical design discussions and mentor other data engineers.
• Troubleshoot production data pipeline issues and improve overall platform performance.