Senior Data Engineer, (Senior Consultant, Data & Analytics)

Fearless•Baltimore, MD

2d•$120,588 - $195,955•Remote

About The Position

We are seeking a Senior Data Engineer as part of our Senior Consultant, Data and Analytics career path to design and build cloud-native data pipelines and analytics solutions supporting federal and state customers. As a Senior Data Engineer, you will serve as a technical leader responsible for architecting modern data platforms, guiding engineering decisions, and delivering scalable, secure, mission-critical solutions across a variety of customer environments. You will partner with engineers, architects, product teams, and customer stakeholders to modernize enterprise data ecosystems, design high-performance analytics platforms, and develop innovative data solutions that enable informed, data-driven decision-making. You will help establish engineering best practices, mentor fellow engineers, and influence the technical direction of enterprise data platforms supporting public sector missions across healthcare, financial services, and other regulated industries. This is a fully remote role. At Fearless, the Senior Consultant is an expert in data analysis, engineering, and science. This role will possess the necessary skills at an advanced level of proficiency to drive organizational transformation by architecting scalable data platforms, building advanced machine learning solutions, and mentoring customers and teams to become data-driven organizations. The role involves working with executives, project teams, and internal leaders across departments to identify opportunities where data can accelerate missions, create efficiencies, and deliver technical excellence.

Requirements

Minimum of 7 years of experience in data engineering or related roles.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field, or equivalent work experience.
Demonstrated experience architecting cloud-native data platforms in AWS environments.
Expert-level proficiency with Python, SQL, Apache Spark, and distributed data processing frameworks.
Experience designing and implementing modern data lake, lakehouse, and enterprise data platform architectures.
Experience building scalable ETL/ELT frameworks supporting large, complex datasets.
Experience implementing data governance, metadata management, data lineage, and data quality solutions.
Experience designing secure, highly available, and scalable cloud-native architectures.
Experience implementing CI/CD pipelines and Infrastructure as Code using tools such as Terraform or AWS CDK.
Strong understanding of cloud networking, security, IAM, and data platform best practices.
Demonstrated ability to lead technical initiatives and influence architecture decisions across engineering teams.
Experience partnering directly with technical and non-technical stakeholders to translate business requirements into technical solutions.
Excellent communication skills with the ability to present complex technical concepts to diverse audiences.

Nice To Haves

Hands-on experience with Databricks, Unity Catalog, Delta Lake, or similar modern lakehouse technologies.
Experience with Apache Airflow, dbt, Kafka, Snowflake, Redshift, Iceberg, or comparable modern data platforms.
Experience implementing streaming and event-driven data architectures.
Experience designing Data Mesh, Data Fabric, or domain-oriented data architectures.
Experience with graph databases, vector databases, semantic search, or AI/ML data platforms.
Experience supporting analytics, machine learning, or Generative AI workloads.
Experience building data observability, monitoring, and operational excellence capabilities.
Experience supporting cloud modernization initiatives within federal or state government agencies.
Experience working in highly regulated environments requiring secure, compliant cloud architectures.
AWS Certified Data Analytics – Specialty, AWS Solutions Architect, Databricks Data Engineer Professional, or equivalent cloud certifications.

Responsibilities

Lead the architecture, design, and implementation of enterprise-scale cloud-native data platforms supporting mission-critical workloads.
Design and develop scalable data pipelines supporting structured, semi-structured, and unstructured data ingestion, transformation, and delivery.
Architect modern lakehouse and data platform solutions using Databricks, Delta Lake, AWS, and open data technologies.
Partner with customer stakeholders to understand business objectives and translate them into scalable technical solutions.
Drive architectural decisions around data modeling, governance, security, observability, scalability, and cost optimization.
Design and implement robust ETL/ELT frameworks supporting enterprise analytics and operational reporting.
Develop high-performance data processing solutions using Python, SQL, Spark, and distributed computing technologies.
Build reusable data engineering frameworks, standards, and accelerators that can be leveraged across multiple customer engagements.
Implement metadata management, data lineage, cataloging, and data quality controls to support trusted enterprise data platforms.
Develop Infrastructure as Code (IaC) and CI/CD pipelines to automate deployment, testing, and operational management of cloud data solutions.
Optimize data platforms for reliability, scalability, performance, and cloud cost efficiency.
Collaborate with solution architects, software engineers, DevOps engineers, and security teams to deliver integrated cloud-native solutions.
Lead technical design sessions, architecture reviews, and code reviews while promoting engineering excellence across delivery teams.
Mentor and coach data engineers by providing technical guidance, best practices, and career development support.
Evaluate emerging technologies and recommend innovative approaches that improve platform capabilities and customer outcomes.
Support Agile delivery teams while helping shape engineering processes and technical standards across multiple programs.