Technology Lead | ETL & Data Quality | ETL - Others

"Project Niche Skills"
VISA DEPENDENT candidates are allowed
POC - Diksha

Job Details:

Job title: Product Owner
Work Location with Zip code: 77002
Duration of the project: 6
This role requires any travel (Yes/No): NO
Please confirm if this would be onsite from day one OR Initial remote for 2-3 months, and then expect the candidate to work from the office OR 100% Remote:
Is this a 100% remote role? NO
Vendor rate range: ####- 88
Minimum years of experience required: 12
Would you require the candidates to meet you for an in-person interview?: No
Education Qualification required: Bachelor s / Masters : Bachelors

Must have skills:
Strong experience with Snowflake, AWS (S3, Glue, Lambda, IAM, Redshift/Athena), and DBT.
Proficiency in SQL and Python.
Knowledge of data warehousing, dimensional modeling, and data lake architectures.
Experience with Git, CI/CD pipelines, and Agile delivery methodologies.
Understanding of data governance, security, and performance tuning.
Preferred Skills
Experience with Airflow, Informatica, Matillion, Qlik Replicate, or Kafka.
Exposure to DataOps, AI/ML, and real-time data processing.

Detailed Job Description:
Design, develop, and optimize scalable cloud-based data pipelines and data warehouse solutions using Snowflake, AWS, and DBT. Support enterprise analytics, reporting, and AI/ML initiatives by ensuring reliable, high-quality, and performant data platforms.
Success Metrics
Data pipeline reliability and SLA adherence.
Reduction in data processing and query execution times.
Data quality and accuracy improvements.
Increased automation and reduced manual operational effort.
Minimum years of experience:
8-12+ years in Data & Analytics.
3-5+ years as a Product Owner, Product Manager, or Business Lead for Data & AI initiatives.

Education: Bachelor s / Masters

The top 3 responsibilities would expect the Subcon to shoulder and execute:
Develop and maintain ELT/ETL pipelines using DBT and AWS services.
Design, build, and optimize Snowflake data models, schemas, and performance.
Integrate data from diverse source systems into cloud data platforms.
Implement data quality, monitoring, and governance controls.
Collaborate with Product Managers, Data Architects, Analysts, and Business stakeholders.
Support production deployments, incident resolution, and platform optimization.
Automate data engineering processes using CI/CD and DevOps practices.

Project Code: Waste Management National Services