Remote Data Engineer - Cloud ETL & Analytics

Fearless Solutions; Baltimore (MD); On-site; USD 96,000 - 162,000; Full time

Benefits offered by this job

Flexible work arrangements

Medical insurance

401(k) with match

Job summary

Fearless is seeking a Data Engineer (Consultant, Data & Analytics) to design, build, and optimize scalable data infrastructure powering federal health analytics. You will develop secure pipelines using Python, SQL, PySpark, and Databricks in cloud-native environments, collaborating with data scientists and stakeholders to enable data-driven decision making while complying with federal standards.

The role emphasizes data quality, automation, and building compliant, auditable data platforms that

Qualifications

• Minimum 4 years of experience in data engineering or related roles.
• Ability to obtain and maintain Public Trust clearance.
• Strong experience with ETL/ELT pipeline design, data modeling, workflow orchestration, and distributed data processing.
• Experience with cloud-based data ecosystems such as Azure, AWS, or GCP.
• Strong programming experience with Python, SQL, and Spark.
• Experience integrating machine learning pipelines with data engineering workflows.
• Experience working with message queuing, stream processing, and large-scale data systems.
• Strong understanding of data quality, data validation, and data governance practices.
• Experience with SQL and NoSQL databases such as Postgres, MySQL, MongoDB, or Cassandra.
• Experience working in Agile delivery environments.

Responsibilities

• Design, build, and maintain scalable, distributed data pipelines and ETL/ELT workflows that support public health analytics.
• Implement data modernization methods using APIs, machine learning technologies such as NLP, and artificial intelligence capabilities.
• Build secure, fault-tolerant, compliant data infrastructure aligned with customer and federal standards.
• Assemble, clean, and optimize large, complex datasets to meet functional and non‑functional business requirements.
• Develop automated processes for data delivery, data quality validation, and performance optimization.
• Utilize PySpark or Databricks to build and maintain distributed data processing pipelines.
• Collaborate with data scientists, analysts, and cross‑functional teams to improve data models and support data‑driven decision making.
• Support ad‑hoc data and analysis needs to better understand program and customer behaviors.
• Evaluate tools, technologies, and platforms for data ingestion, machine learning, orchestration, and analytics.
• Monitor data volume, data quality, data pipeline performance, and system reliability.
• Troubleshoot and resolve data and pipeline issues, performing root‑cause analysis where needed.
• Build analytical tools, dashboards, and interfaces that deliver actionable insights to stakeholders.
• Document technical processes, data flows, and pipelines in accordance with customer documentation requirements.
• Ensure all electronic deliverables are Section 508 compliant.
• Follow federal security, encryption, and records management requirements including FIPS‑validated encryption, Privacy Act compliance, and NARA standards.

Skills

Python

SQL

PySpark

Databricks

Cloud platforms

Data modeling

ETL/ELT

Education

Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, or related field

Tools

Azure/AWS/GCP

Job description

Fearless is seeking a Data Engineer (Consultant, Data & Analytics) to design, build, and optimize scalable data infrastructure powering federal health analytics. You will develop secure pipelines using Python, SQL, PySpark, and Databricks in cloud-native environments, collaborating with data scientists and stakeholders to enable data-driven decision making while complying with federal standards.

The role emphasizes data quality, automation, and building compliant, auditable data platforms that