Lead Data Engineer (Hybrid)

CareFirst•Reston, VA

12h•Hybrid

About The Position

The Lead Data Engineer is responsible for orchestrating, deploying, maintaining, and scaling Cloud or on-premise infrastructure targeting big data and platform data management (Relational and NoSQL, distributed and converged) with emphasis on reliability, automation, and performance. This role will focus on leading the development of solutions and helping transform the company's platforms to deliver data-driven, meaningful insights and value to the company.

Requirements

Bachelor's Degree in Computer Science, Information Technology or Engineering or related field OR in lieu of a bachelor's degree, an additional 4 years of relevant work experience is required in addition to the required work experience.
8 years' Experience in leading data engineering and cross functional team to implement scalable and fine-tuned ETL/ELT solutions for optimal performance.
Experience developing and updating ETL/ELT scripts.
Hands-on experience with application development, relational database layout, development, data modeling.
Knowledge and understanding of at least one programming language (i.e., SQL, NoSQL, Python).
Knowledge and understanding of database design and implementation concepts.
Knowledge and understanding of data exchange formats.
Knowledge and understanding of data movement concepts.
Strong technical and analytical and problem-solving skills to troubleshoot to solve a variety of problems.
Requires strong organizational and communication skills, written and verbal, with the ability to handle multiple priorities.
Able to effectively provide direction to and lead technical teams.
Must be eligible to work in the U.S. without Sponsorship.

Nice To Haves

Proven hands-on experienced AWS Redshift Administrator to engineer, operate, secure, and optimize our cloud data warehouse platform.
AWS Certified Data Analytics Specialty
AWS Certified Solutions Architect
Experience in regulated or enterprise environments.
Experience with cloud migrations.
Experience with data lake architectures.
Hands-on experience administering Redshift at enterprise scale (RA3/Serverless/Spectrum), including scaling, patching, backups/snapshots, and disaster recovery.
Proven Redshift performance tuning (WLM/concurrency scaling, sort/dist keys, compression/encoding) with a focus on cost optimization.
Strong security and governance background (IAM, KMS/TLS encryption, row/column-level controls, audit logging, compliance).
Experience integrating Redshift with S3/Glue/Lake Formation and supporting ETL/ELT pipelines, Spectrum, and external schemas.
Automation/DevOps mindset: Infrastructure as Code (Terraform/CloudFormation) plus CI/CD and blue/green deployment practices.
Operational excellence: CloudWatch/system-view monitoring, alerting, incident response, and root-cause analysis.
Experience with data warehousing best practices (dimensional modeling, ELT patterns, data quality checks, and SLAs).
Proficiency with scripting for automation and operations (Python and/or Bash), including building runbooks and self-healing jobs.
Strong AWS networking fundamentals supporting Redshift (VPC, subnets, security groups, routing, PrivateLink/VPC endpoints).
Background in warehouse migrations/modernization (e.g., sizing, cutover planning, data validation, and rollback strategies).
Experience designing workload isolation and service tiers (WLM queues, query priorities, and governance for multi-team usage).
Familiarity with common analytics engineering and orchestration tools (dbt, Airflow, Glue workflows, Step Functions).
Strong observability/FinOps practices for data platforms (dashboards, SLOs, anomaly detection, chargeback/showback).
Excellent documentation and stakeholder communication skills, including publishing standards, architecture diagrams, and operational playbooks.

Responsibilities

Lead the team to design, configure, implement, monitor, and manage all aspects of the Data Integration Framework.
Define and develop Data Integration best practices for the data management environment for optimal performance and reliability.
Develop and maintain infrastructure systems (e.g., data warehouses, data lakes) including data access APIs.
Prepare and manipulate data using Hadoop or equivalent MapReduce platform.
Provide detailed guidance and perform work related to Modeling Data Warehouse solutions in the Cloud or on-premise.
Understand Dimensional Modeling, De-normalized Data Structures, OLAP, and Data Warehousing concepts.
Oversee the delivery of engineering data initiatives and projects.
Support long term data initiatives as well as Ad-Hoc analysis and ELT/ETL activities.
Create data collection frameworks for structured and unstructured data.
Apply data extraction, transformation, and loading techniques to connect large data sets from a variety of sources.
Enforce the implementation of best practices for data auditing, scalability, reliability, and application performance.
Develop and apply data extraction, transformation, and loading techniques to connect large data sets from a variety of sources.
Interpret data, analyze results using statistical techniques, and provide ongoing reports.
Execute quantitative analyses that translate data into actionable insights.
Provide analytical and data-driven decision-making support for key projects.
Design, manage, and conduct quality control procedures for data sets using data from multiple systems.
Improve data delivery engineering job knowledge by attending educational workshops; reviewing professional publications; establishing personal networks; benchmarking state-of-the-art practices; participating in professional societies.