About The Position
The Senior Data Engineer is a strong technical practitioner who builds, optimizes, and modernizes data pipelines, data platforms, and analytics infrastructure for enterprise clients. This role is hands-on, delivery-driven, and takes pride in engineering data systems that are reliable, scalable, and built to last. The engineer works across the full data engineering lifecycle—from ingestion and transformation to platform implementation and analytics enablement—and possesses enough architectural instinct to contribute to solution design conversations. 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.
Requirements
- 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
Nice To Haves
- 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
Responsibilities
- 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
- 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
- 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
- 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
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
