Data Integration Engineer

Job Summary (List Format):

- Minimum 8+ years' experience in Data Management, with strong background in analytical data warehousing and enterprise-scale data platforms.
- At least 3+ years of hands-on experience with Snowflake Cloud Data Warehouse, including advanced features and performance optimization for large analytics workloads.
- Strong expertise in AWS cloud architecture, designing scalable, secure, and cost-efficient data solutions using core AWS services.
- Responsible for designing, developing, and maintaining complex SQL and PL/SQL procedures for high-performance data integration and transformation.
- Develop data integration solutions using Snowflake tools (Snowpark, Streams, Tasks, Snowpipe) within AWS for both real-time and batch pipelines.
- Proficient in logical and physical data modeling to support analytics, reporting, and BI solutions.
- Experience in developing dimensional data models, including SCDs and multiple fact tables, aligned with analytics best practices.
- Manage the full data lifecycle: lineage, profiling, mapping, integration, validation, cleansing, masking, subsetting, archiving, purging, virtualization, and metadata management.
- Ensure data quality through implementation and adherence to testing standards (unit, integration, UAT).
- Perform SQL performance tuning, proactive monitoring, and root cause analysis for data pipeline and platform issues.
- Actively participate in Agile teams, contributing to sprint planning, implementation, estimation, and documentation using Jira.
- Collaborate with peers, architects, and stakeholders, providing guidance and conducting code reviews for junior engineers.
- Demonstrate strong verbal and written communication skills, partnering with both technical and business teams.
- Hybrid work model: 3 days onsite, video interviews, anticipated start in 2-4 weeks.
- No visa restrictions; LinkedIn profile required for all candidates.