Lead Data Engineer

Lead Data Engineer with strong expertise in Python and DBT to design and develop a scalable, reusable, and technology-agnostic data ingestion framework.

The role involves replacing existing ETL capabilities with an open-source technology stack leveraging Python, DBT, and orchestration technologies such as Dagster or Control-M. The solution will utilize AWS S3 as the data Lakehouse storage layer and Snowflake as the target data platform.

The ideal candidate should have hands-on experience building enterprise-grade data ingestion frameworks, developing reusable connectors, implementing metadata-driven pipelines, and integrating heterogeneous source systems with modern cloud data platforms., 1. Data Ingestion Framework Development

• Develop a reusable, scalable, and technology-agnostic data ingestion framework using Python.
• Design modular and extensible components to support multiple source systems.
• Develop metadata-driven and configuration-based ingestion capabilities.
• Build reusable connectors and adapters for different source technologies.
• Implement full and incremental data ingestion patterns, including CDC where applicable.
• Implement robust error handling, logging, auditing, retry, and reprocessing mechanisms.

1. Data Integration & Lakehouse Architecture * Develop ingestion pipelines to integrate data from heterogeneous source systems into AWS S3 and Snowflake. * Implement data ingestion from SQL Server, Oracle, PostgreSQL, REST APIs, SFTP, AWS S3, and other source systems. * Design and implement data lakehouse ingestion patterns using AWS S3. * Work with file formats such as CSV, JSON, Parquet, and other structured/semi-structured formats. * Implement schema handling, data validation, partitioning, and data movement strategies.

2. Python & dbt Engineering * Develop production-grade Python applications and reusable ingestion libraries. * Build and maintain dbt models for data transformation and integration. * Implement dbt testing, documentation, incremental models, and reusable macros. * Optimize Python-based ingestion processes and dbt transformations for performance and scalability. * Follow engineering best practices for modularity, maintainability, and code quality.

3. Workflow Orchestration * Implement and manage workflow orchestration using Control-M and Dagester. * Develop reusable workflows for scheduling, dependency management, retries, and pipeline monitoring. * Integrate orchestration workflows with Python ingestion components and Snowflake.

4. Testing, Deployment & Production Support * Develop unit tests and integration tests for ingestion framework components. * Support migration and deployment across DEV, SIT, UAT, and PROD environments. * Collaborate with QA teams to perform functional, integration, regression, and performance testing. * Troubleshoot ingestion failures, data discrepancies, and performance bottlenecks. * Support production deployment, stabilization, documentation, and knowledge transfer.

5. Technical Leadership & Collaboration * Work closely with the onshore solution architect, customer stakeholders, and offshore engineering teams. * Provide technical guidance, code reviews, and engineering best practices. * Participate in architecture discussions, technical design reviews, and sprint planning. * Mentor developers and ensure adherence to established engineering standards.

• Python: Expert-level hands-on experience in Python development, including object-oriented programming, reusable libraries, APIs, error handling, and performance optimization.
• DBT: Strong hands-on expertise in DBT Core, including models, macros, tests, incremental processing, and deployment practices.
• Data Engineering: Extensive experience building enterprise-scale ETL/ELT pipelines and reusable data ingestion frameworks.
• Snowflake: Strong experience with Snowflake architecture, data loading, stages, Snowpipe, SQL, and performance optimization.
• AWS: Hands-on experience with AWS S3, IAM, and cloud-based data integration patterns.
• Data Integration: Experience integrating relational databases, REST APIs, SFTP, and cloud-based storage systems.
• SQL: Advanced SQL development and query optimization skills.
• CI/CD: Experience with Git-based version control, automated builds, testing, and deployment pipelines.

Good-to-Have Skills

• Hands-on experience with Dagster for data orchestration and asset-based pipeline development.
• Experience with utilizing Control-M JOB scheduler.
• Experience with Apache Iceberg and open table formats.
• Experience implementing metadata-driven ingestion frameworks.
• Experience migrating ETL workloads from SnapLogic or similar integration platforms.
• Experience with Docker, Terraform, and infrastructure automation.
• Experience implementing data quality, observability, and monitoring solutions.
• Experience working in an Agile environment utilizing SCRUM, * 10-12 years of overall experience in data engineering and data integration.
• Strong architectural and hands-on engineering capabilities.
• Experience working on enterprise data modernization initiatives.
• Excellent communication and stakeholder management skills.
• Ability to work independently in a distributed onshore/offshore delivery model.

Mandatory Skills

1. Expert-level Python development.
2. Strong hands-on dbt experience.
3. Enterprise data ingestion framework development.
4. Snowflake and AWS S3 experience.
5. Strong SQL and data integration experience.