Lead Data Engineer

Lead Data Engineer – AWS Redshift, Oracle & Airflow
Employment Type:
Full-Time | Permanent
Location:
Pakistan (Lahore / Karachi / Islamabad) – Hybrid
Role Overview
We are seeking a
Lead Data Engineer
to drive the end-to-end modernization of a legacy Oracle platform into a scalable data warehouse built on Amazon Redshift. This role involves leading, accessing oracle procedural workloads, extracting business logic/rules, object dependencies and redesign/convert into Amazon Redshift-native, set-based, parallelized data processing with production-grade orchestration, validation, and operational controls integrated with workflow orchestration using Apache Airflow (MWAA).
Performance enhancement is one of the primary objectives by leveraging Redshift’s parallel processing capabilities. The Lead Data Engineer will be responsible for end-to-end delivery, including solution architecture, code conversion/refactoring to deliver enterprise-grade solution adhering to best practices/standards in performance optimization, scalability, and orchestration.
Experience
10+ years of experience in Data Engineering, Data Warehousing, and Database Development
.
Primary Skills
AWS Data Engineering, Data Architecture, Data Warehousing, Procedural SQL Development, Amazon Redshift, Airflow Orchestration, Python
Lead end-to-end modernization of database programming objects and business logic from Oracle into Amazon Redshift and MWAA-based orchestration
Strong Amazon Redshift expertise, including schema design, MPP concepts, sort / distribution strategy, datashares, serverless or provisioned deployment models, and query tuning
Converting procedural database logic into set-based and orchestration-driven workflows
Technologies
Must Have: Redshift, MPP, Oracle PL/SQL, PL/pgSQL, DAG, Airflow (MWAA)
Nice To Have: AWS (S3, Lambda), SQL, ETL/ELT, Data Modeling, Query Optimization, Performance Tuning, Linux/Unix, Git, CI/CD

Key Responsibilities

Perform deep code analysis of Oracle PL/SQL objects to determine what can be translated, what must be refactored, and what must be re-architected
Identify patterns that are incompatible or inefficient in Redshift, including nested loops, row-by-row processing, procedural orchestration, excessive temp-table chaining, exception-driven logic, and transaction-dependent flows
Understand, transform, convert and optimize complex Oracle PL/SQL procedures, functions, and scripts & storage objects to AWS Redshift to deliver the most optimal performance for Redshift MPP architecture
Create design artifacts such as object inventory, dependency maps, pseudo-code, migration strategy, and Redshift rewrite patterns
Implement production-ready SQL and Airflow DAGs with clear operational behavior, logging, alerting, and restartability
Tune execution to meet SLA targets through parallelism, query optimization, workload isolation, and efficient data movement
Review code quality, enforce engineering standards, and mentor junior or mid-level engineers contributing to the migration
Architect and enhance analytics platforms built on Amazon Redshift
Define and enforce best practices for data governance, security, and data quality
Collaborate with cross-functional stakeholders to design and deliver robust data solutions
Provide technical leadership by mentoring engineers, conducting code reviews, and guiding best practices
Key Requirements
Strong hands-on experience with Amazon Redshift, Oracle PLSQL, PL/pgSQL, Amazon MWAA (Apache Airflow), Aurora PostgreSQL, AWS DMS, S3, Lambda, Python, CloudWatch, GitHub
Proven experience in
enterprise-scale data warehouse architecture
Expertise in
performance tuning and large-scale data processing
Prior experience in
technical leadership or mentoring roles
Deep hands-on experience with Oracle and/or PostgreSQL database programming, especially stored procedures, functions, cursors, transaction logic, and performance troubleshooting