
Senior Data Engineer
Job Description
Position:
Senior Data EngineerJob Description:
our data pipelines, data models, and analytics infrastructure. The ideal candidate has
strong experience in building scalable data platforms across operational systems and
enterprise data warehouses.
Key Responsibilities
Core Responsibilities
• Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Develop high-quality data integration workflows using modern data engineering tools and frameworks.
• Optimize data processing performance, storage usage, and pipeline reliability.
• Implement data quality checks, validation rules, and monitoring dashboards.
• Collaborate with data analysts, data scientists, and business teams to deliver reliable datasets suitable for reporting and analytics.
• Manage data security, access controls, and governance best practices.
• Support production workflows, troubleshoot issues, and improve platform stability.
Additional Responsibilities for Data Models & Structures (Operational + DW)
• Own the design and maintenance of logical and physical data models for
operational systems and enterprise data warehouse (OLTP + DWH).
• Define and enforce data modeling standards, naming conventions, and schema governance.
• Translate business processes into efficient database schemas, including star/snowflake models.
• Optimize queries and database structures for high-performance analytics and real-time data access.
• Work closely with DBAs, architects, and product teams to ensure data models meet scalability and performance requirements.
• Perform impact analysis for schema changes across upstream and downstream systems.
Requirements
Must-Have Requirements
• 5+ years of experience as a Data Engineer or similar role.
• Strong Python Skills and Experience
• Strong SQL /PLSQL skills and experience with relational databases (Oracle, PostgreSQL, MySQL, or similar).
• Experience with NoSQL databases and streaming systems (Kafka, Kinesis, etc.).
• Hands-on experience with data processing frameworks (Spark, Databricks, or similar).
• Proficiency with cloud platforms (AWS).
• Experience with workflow orchestration (Airflow, Prefect, or similar).
• Strong understanding of ETL/ELT patterns, data warehousing concepts, and BI fundamentals.
• Proven ability to design robust data models and optimize database structures.
• Knowledge of data governance, cataloging, and metadata management.
• Familiarity with CI/CD, infrastructure-as-code, and DevOps practices.
• Experience with real-time analytics or high-volume distributed data systems