Data Engineer

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

Minimum Requirements:

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