Lead Data Engineer-- Data Ingestion Framework
Boston, MA
Long Term
Contract
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.
Key Responsibilities
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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
Required Technical Skills
- 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
Qualifications
- 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
- Expert-level Python development.
- Strong hands-on dbt experience.
- Enterprise data ingestion framework development.
- Snowflake and AWS S3 experience.
- Strong SQL and data integration experience.
Munesh
,
CYBER SPHERE LLC
