Responsibilities:

Data Engineering & Architecture:

• Design, build, and optimize scalable, secure, and repeatable data pipelines using AWS (S3, Glue, Lambda, Step Functions, Redshift, IAM) and Databricks (PySpark,Lakeflow,Delta Lake, Unity Catalog).

• Serve as the technical leader for data ingestion pipelines, modeling new datasets such as Advisor360, CRM, ETF, and Mutual Fund platforms.

• Apply strong data modeling (dimensional, canonical, and domain-driven) principles to support analytics, reporting, and AI/ML use cases.

• Ensure alignment with enterprise data architecture standards, promoting reusability, governance, and long-term maintainability.

Consultative Problem Solving:

• With limited guidance, independently perform deep investigations, identify data issues, and propose solutions that balance performance, cost, risk, and business needs.

• Engage business stakeholders to gather ambiguous requirements, ask the right questions, and translate them into clear technical designs.

• Provide thought leadership and recommend technical patterns, frameworks, and toolsets.

Data Quality, Reliability & Operations:

• Implement robustdata quality frameworks, monitoring, and alerting to ensure high trust in business-critical data assets.

• Troubleshoot data inconsistencies and ensure proper logging, testing, and recovery mechanisms across pipelines.

• Lead regression testing, software upgrades, and production deployments with strong change control discipline.

Collaboration & Leadership:

• Lead all phases of solution development—from design to deployment and operationalization.

• Mentor and guide other engineers in coding standards, architecture patterns, Databricks best practices, and AWS platform usage.

• Partner with Data Architecture, Analytics, Product, and Business teams to deliver solutions that improve decision-making.

• Provide training sessions and documentation to uplift the data engineering maturity across the organization.

Special Projects:

• Participate in strategic initiatives such as AI readiness, data unification efforts, metadata strategy, and enterprise integration roadmaps.

• Drive continuous improvement in engineering frameworks, onboarding workflows, and platform capability.