Strong expertise in Snowflake Data Warehouse and its components.
Advanced SQL skills (CTEs, window functions, tuning) and Python for pipelines and data processing.
Experience with dbt, AWS Glue/Lambda/S3, Rockset, and Amazon QuickSight for analytics and visualizations.
Requirements:
Lead and mentor a data engineering team, driving end-to-end data solutions using Snowflake and Python.
Design, develop, and maintain robust ETL/ELT pipelines (dbt, AWS Glue/Lambda/S3) ensuring data integrity and rapid issue resolution.
Collaborate with cross-functional teams and stakeholders to translate business goals into scalable data architectures and dashboards (Amazon QuickSight).
Advance automation, CI/CD practices, data governance, security, and adoption of Big Data technologies to optimize the data platform.
Job description
🌟 Distinguished Tech Innovator:3Pillar warmly extends an invitation for you to join an elite team of visionaries. Beyond software development, we are dedicated to engineering solutions that challenge conventional norms. Envision you: steering projects that redefine urban living, establish new media channels for enterprise companies, or drive innovation in healthcare. Your invaluable expertise will serve as the cornerstone in shaping the future direction of our endeavors.This role transcends the ordinary realms of coding; it's about orchestrating technological marvels that disrupt industries. Seize this extraordinary opportunity to lead a team that is actively shaping the tech landscape for our clients, and sets global standards along the way. 🌍🔥
Minimum Qualification
Experience: 5-10 years of experience in data engineering,
Strong exp in Snowflake DWH and its components
SQL — advanced (CTEs, window fns, tuning)
Amazon QuickSight (BI visualizations)
Python (pipelines, data processing)
Data integrity & root cause analysis
ETL / ELT pipeline development
dbt (data build tool)
AWS Glue / Lambda / S3
Rockset (analytical engine)
SQL-heavy role.
Snowflake critically important.
Amazon QuickSight used to render visualizations for clinical stakeholders.
Additional experience
Data Architecture: Experience designing or optimizing data lake solutions.
Security Practices: Understanding of data security practices, data governance, and compliance for secure data processing.
Automation & CI/CD: Familiarity with CI/CD tools to support automation of deployment and testing.
Big Data Technologies: Knowledge of big data processing tools like Spark, Hive, or related AWS services.
Advanced Analytics: Background in analytics or data science to contribute to more data-driven decision-making.
Cross-Functional Collaboration: Experience collaborating with non-technical teams on business goals and technical solutions.
