Senior Data Engineer (H1B transfer - India based Architect Senapathi Thirumurugan)

Presidio, Where Teamwork and Innovation Shape the Future
At Presidio, we're at the forefront of a global technology revolution, transforming industries through cutting-edge digital solutions and next-generation AI. We empower businesses - and their internal customers - to achieve more through innovation, automation, and intelligent insights.

The Role

The Senior Data Engineer is a strong technical practitioner who builds, optimizes, and modernizes data pipelines, data platforms, and analytics infrastructure for enterprise clients. You are hands-on, delivery-driven, and take pride in engineering data systems that are reliable, scalable, and built to last. 

You work across the full data engineering lifecycle—from ingestion and transformation to platform implementation and analytics enablement—and you bring enough architectural instinct to contribute to solution design conversations, not just execute what's handed to you. You understand the difference between code that works and code that holds up in production under real client conditions. 

This role sits within Presidio Digital's Data & Analytics practice, operates as a billable client-facing engineer across engagements spanning data platform modernization, pipeline development, AI-ready data foundations, and analytics delivery.

Responsibilities Include:

Data Engineering & Platform Development 

• Design, build, and maintain scalable data pipelines and workflows across modern cloud data platforms—Snowflake, Databricks, Microsoft Fabric, or equivalent
• Implement ELT/ETL processes with a focus on data quality, performance, reliability, and maintainability
• Assemble and transform large, complex datasets that meet both functional and non-functional business requirements
• Build and optimize data models to support analytics, reporting, and AI/ML use cases
• Work across cloud environments (AWS, Azure, GCP) and their native data services

Architecture Contributions 

• Contribute to solution design discussions alongside architects—bring engineering-level perspective on feasibility, complexity, and implementation trade-offs
• Help define data pipeline patterns, platform configurations, and engineering standards within the engagement
• Identify opportunities to improve data infrastructure: automate manual processes, improve data delivery, redesign for greater scalability and performance 

Analytics & Insights Enablement 

• Build analytics tools and data products that surface actionable insights for clients across key business metrics
• Support integration with BI and visualization tools (Power BI, Tableau, Looker, Qlik, or similar)
• Ensure data products are well-documented, governed, and ready for downstream consumption 

Client Engagement & Pre-Sales Support 

• Participate in client discovery and requirements-gathering sessions; contribute an engineering-level perspective on feasibility, complexity, and implementation approach
• Support pre-sales and scoping activities alongside Architects and Pre-Sales teams—help validate that proposed solutions are technically achievable before commitments are made
• Engage directly with client technical teams throughout the engagement lifecycle; build credibility through engineering quality and clear communication
• Work effectively across multiple client engagements at different stages of the implementation lifecycle 

Delivery & Collaboration 

• Collaborate with architects, solution owners, and client technical teams to deliver against agreed outcomes
• Mentor junior data engineers; share knowledge and raise the engineering quality of the teams you work with
• Communicate technical progress, blockers, and decisions clearly to both technical and non-technical stakeholders

Required Skills and Professional Experience:

• Bachelor's Degree or equivalent experience and / or military experience
• 5+ years in data engineering or cloud data platform development roles
• 6+ years of advanced SQL knowledge across multiple database environments and data modeling patterns
• Hands-on experience developing on modern cloud data platforms—Snowflake, Databricks, or equivalent; production-grade implementation experience, not just familiarity
• Experience with cloud data stacks on AWS, Azure, and/or GCP (e.g., EMR, Redshift, Glue, Kinesis/Kafka, Azure Data Factory, Synapse, BigQuery, Dataproc)
• Strong experience building data pipelines on Spark; proficiency