The RoleThe 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 in Python and/or Scala
- Experience with data pipeline orchestration tools (Airflow, dbt, or similar)
- Familiarity with lakehouse architectures, data mesh/fabric patterns, and modern data modeling approaches
- Exposure to AI-ready data engineering-building pipelines and data foundations that support GenAI, Agentic AI, and ML workloads
- Solid communication skills; ability to work with both technical teams and business stakeholders across client engagements
Preferred Skills and Professional Experience:- Experience with streaming and real-time data architectures (Kafka, Kinesis, Spark Streaming)
- Exposure to BI and analytics tooling: Power BI, Tableau, Looker, Qlik, or similar
- Consulting or professional services background
- Experience contributing to data architecture discussions and solution design
- Familiarity with data governance, data quality frameworks, and lineage tools
- Experience with infrastructure-as-code and DevSecOps practices for data pipelines
Who You Are:- Curious - you want to understand how the full data system works, not just your pipeline; you dig into problems and don't stop at the surface
- Accountable - you own your code, your pipelines, and your delivery commitments; you flag issues early and drive them to resolution
- Consultative - you listen to understand the client's real problem before jumping to a solution; you ask the right questions and help clients think through trade-offs
- A continuous learner - the data and AI engineering landscape evolves fast; you stay current and apply new approaches where they genuinely add value
- Effective in ambiguity - client data environments are rarely clean or well-documented; you can move forward, make reasonable decisions, and ask the right clarifying questions without getting stuck
- A clear communicator - you can explain what you built, why you built it that way, and what the trade-offs were to both engineers and business stakeholders; you adapt your language to your audience
- A team player - you collaborate openly, give and receive feedback constructively, and treat delivery as a shared outcome with your colleagues and clients
- Detail-oriented but pragmatic - you care about quality and correctness, and you also know when good enough is the right call to keep delivery moving
- Client presence - you are the engineering face of Presidio on client engagements; you communicate with professionalism, manage expectations proactively, build trust through the quality of your work, and represent the team well in every client interaction
