Seeking an experienced Senior Data Engineer to design, development, and optimization of
our data pipelines, data models, and analytics infrastructure. The ideal candidate has
strong experience in building scalable data platforms across operational systemsand
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.

•Lead database refactoring efforts, schema evolution, and versioning processes.
•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/PLSQLskills 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.

Nice-to-Have Skills
•Familiarity with CI/CD, infrastructure-as-code, and DevOps practices.
•Experience with real-time analytics or high-volume distributed data systems