Data Engineer

Mondo; Baltimore (MD); Hybrid; USD 100,000 - 150,000; Full time

Benefits offered by this job

Medical

Dental

Vision

401(k)

PTO

Parental Leave

Additional Company Benefits

Job summary

Mondo is seeking an experienced Data Engineer to join our team in Baltimore, offering a hybrid schedule with 50-75% onsite work and regular travel to client sites. You will design, build, and maintain secure data platforms, pipelines, and CI/CD processes in a federal-adjacent, enterprise environment.

The ideal candidate has 5+ years of data engineering experience, strong Python and SQL skills, and hands-on experience with Spark/PySpark, Kafka, Databricks, and dbt across cloud platforms

Qualifications

• 5+ years of professional data engineering experience.
• M.Sc. required in Data Science, Statistics, Math, CS, or related field; PhD preferred.
• Strong hands-on Python and SQL development skills.
• Experience with Spark/PySpark and distributed data processing.
• Experience with Kafka, event streaming, or comparable streaming technologies.
• Experience designing and building modern data architectures such as lakehouses, data warehouses, data lakes, or distributed data platforms.
• Experience with Databricks, dbt, or comparable modern data technologies.
• Experience with AWS, Azure, and/or GCP.
• Ability to work directly with clients in high-visibility environments.
• Ability to accommodate approximately 50-75% onsite work in Baltimore through rotation.

Responsibilities

• Design, build, and maintain scalable, production-grade data platforms and pipelines.
• Build and enhance CI/CD pipelines supporting reliable data engineering deployments.
• Develop ingestion and transformation pipelines across legacy systems, APIs, IoT/telemetry, relational databases, and other data sources.
• Design cloud-native architectures supporting batch, streaming, and near-real-time workloads.
• Build distributed and event-driven data processing solutions using Spark, Kafka, or equivalent technologies.
• Develop modern lakehouse and data warehouse architectures using Databricks and dbt.
• Write clean, production-quality Python and SQL code.
• Implement automated testing, monitoring, observability, data-quality validation, lineage, and reliability standards.
• Build secure and governed data environments with RBAC, encryption, auditability, and access controls.
• Support analytics and future AI/ML use cases with robust data infrastructure.
• Optimize data platforms for performance, scalability, reliability, and cost.
• Collaborate with engineers, data scientists, and stakeholders to translate requirements into scalable solutions.
• Troubleshoot complex production issues and drive resolution.
• Contribute to architectural decisions and long-term evolution of the data platform.

Skills

Python

SQL

Spark/PySpark

Kafka

Databricks

dbt

CI/CD

Cloud platforms (AWS/Azure/GCP)

Data governance/security

Client-facing collaboration

On-site travel readiness

Education

M.Sc. in Data Science/Statistics/Math/CS

PhD preferred

Tools

AWS

Azure

GCP

Databricks

dbt

Job description

Job Title: Data Engineer
Location-Type: Hybrid / Travel - Baltimore, MD (50-75% onsite)
Work Hours: 40 Hours/Week
Start Date Is: ASAP
Duration: Permanent
Compensation Range: $100,000-$150,000/year
Benefits: Eligible for Medical, Dental, Vision, 401(k), PTO, Parental Leave, and Additional Company Benefits
Must be authorized to work in the U.S. This position is not eligible for sponsorship.

Travel Expectations:

This position requires regular onsite work at the client office in Baltimore, with approximately 50-75% onsite presence. The schedule can be structured rotationally, such as 1-2 weeks onsite followed by 1-2 weeks remote. All required travel and accommodations are covered. Mileage is reimbursed for candidates who drive, while airfare and train travel are booked through the company's travel portal.

Job Description:

Seeking an experienced Data Engineer to join a global technology organization as it expands its U.S. presence and supports a high-impact, federal-adjacent initiative in Baltimore.

This is a hands-on engineering role focused on designing, building, and maintaining secure, scalable data platforms and CI/CD pipelines within a complex client environment. The Data Engineer will work with large and varied datasets across legacy systems, APIs, telemetry/IoT sources, and modern cloud platforms while helping establish reliable infrastructure for analytics and future AI/ML capabilities.

The ideal candidate has strong production data engineering experience, thrives in client-facing environments, and can independently solve complex technical problems while collaborating with distributed teams and stakeholders.

Day-to-Day Responsibilities:

• Design, build, and maintain scalable, production-grade data platforms and pipelines
• Build and enhance CI/CD pipelines supporting reliable data engineering deployments
• Develop ingestion and transformation pipelines across legacy systems, APIs, IoT/telemetry, relational databases, and other data sources
• Design cloud-native architectures supporting batch, streaming, and near-real-time workloads
• Build distributed and event-driven data processing solutions using Spark, Kafka, or equivalent technologies
• Develop modern lakehouse and data warehouse architectures using technologies such as Databricks and dbt
• Write clean, maintainable, production-quality code using Python and SQL
• Implement automated testing, monitoring, observability, data-quality validation, lineage, and reliability standards
• Build secure and governed data environments incorporating RBAC, encryption, auditability, and access controls
• Support data infrastructure that enables analytics and future machine learning and AI use cases
• Optimize data platforms for performance, scalability, reliability, and cost
• Partner with engineers, data scientists, technical teams, and client stakeholders to translate requirements into scalable solutions
• Troubleshoot complex production issues and take ownership of solutions through resolution
• Contribute to architectural decisions and the long-term evolution of the data platform
• 5+ years of professional data engineering experience, including designing and operating production data platforms\
• Education: M.Sc. required (Data Science, Stats, Math, CS, or related quant field); PhD preferred
• Strong hands-on Python and SQL experience
• Strong experience with Spark/PySpark and distributed data processing
• Experience with Kafka, event streaming, or comparable streaming technologies
• Experience designing and building modern data architectures such as lakehouses, data warehouses, data lakes, or distributed data platforms
• Experience with Databricks, dbt, or comparable modern data technologies
• Experience integrating multiple data sources including APIs, legacy systems, relational databases, and/or telemetry/IoT data
• Experience with AWS, Azure, and/or GCP
• Experience building or supporting CI/CD pipelines and production deployment processes
• Understanding of Infrastructure as Code and cloud-native engineering practices
• Experience building highly available, observable, production-grade data systems
• Understanding of data governance, security, access controls, encryption, lineage, and auditability
• Strong troubleshooting, systems-thinking, and problem-solving skills
• Ability to independently own technical deliverables from design through production
• Strong communication and stakeholder collaboration skills
• Comfortable working directly with clients in complex, high‑visibility environments
• Ability to accommodate approximately 50-75% onsite work in Baltimore through a regular travel/onsite rotation

Preferred Qualifications:

• Experience supporting government, public-sector, federal-adjacent, defense, infrastructure, healthcare, financial services, or another regulated environment
• Experience with Databricks, dbt, Docker, Spark/PySpark, and Kafka
• Experience designing secure data platforms subject to regulatory or compliance requirements
• Familiarity with HIPAA, CJIS, FERPA, state privacy requirements, or similar security and privacy frameworks
• Experience with Infrastructure as Code and automated cloud deployments
• Experience supporting analytics, GIS, machine learning, or AI applications through robust data infrastructure
• Experience with IoT, telemetry, infrastructure, or other complex real‑world datasets
• Experience working within globally distributed engineering teams
• Previous consulting or client‑facing engineering experience