Sr. Engineer, Data - Archimedes

Location

Remote restrictions apply

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Salary Estimate

N/A

Seniority

Senior

Tech stacks

Data

Automation

Azure

+56

Visa

U.S. visa required

Permanent role

a day ago

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Archimedes

About Us

Archimedes - Transforming the Specialty Drug Benefit - Archimedes is the industry leader in specialty drug management solutions. Founded with the goal of transforming the PBM industry to provide the necessary ingredients for the sustainability of the prescription drug benefit – alignment, value and transparency – Archimedes achieves superior results for clients by eliminating tightly held PBM conflicts of interest including drug spread, rebate retention and pharmacy ownership and delivering the most rigorous clinical management at the lowest net cost. .____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________. Current associates must use SSO login option at https://employees-navitus.icims.com/ to be considered for internal opportunities.________We are committed to providing equal employment opportunity to all applicants and employees and comply with all applicable nondiscrimination regulations, including those related to protected veterans and individuals with disabilities. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, or handicap.

Pay Range

USD $0.00 - USD $0.00 /Yr.

STAR Bonus % (At Risk Maximum)

0.00 - Ineligible

Work Schedule Description (e.g. M-F 8am To 5pm)

Core Business Hours- Remote or Hybrid 3 Days in Office from our St. Louis, MO or Brentwood, TN offices

Remote Work Notification

ATTENTION: Archimedes is unable to offer remote work to residents of Alaska, Arizona, Arkansas, California, Connecticut, Delaware, Hawaii, Idaho, Louisiana, Maine, Massachusetts, Michigan, Mississippi, Montana, Nebraska, Nevada, New Mexico, New York, North Carolina, North Dakota, Oregon, Rhode Island, South Carolina, South Dakota, Utah, Vermont, Washington, West Virginia, And Wyoming.

Overview

The Sr. Engineer, Data serves as the technical lead for enterprise data engineering, data architecture, lake house platforms, and AI-ready data solutions. This role is responsible for defining enterprise data architecture standards, canonical data models, data governance frameworks, integration patterns, and reusable data products that enable analytics, automation, machine learning, and artificial intelligence initiatives across the organization. Operating within an Azure-first, Databricks-centric environment, the Sr. Engineer provides technical leadership for the organization's transition from traditional SQL-based data architectures to a modern cloud-native lake house platform built on Azure Data Lake Storage Gen2, Azure Databricks, Delta Lake, Unity Catalog, and DataOps automation. The role establishes enterprise standards for data modeling, master data management, metadata management, data lineage, data quality, semantic consistency, and governed data products.

The Sr. Engineer, Data partners closely with business stakeholders, software engineering, analytics, architecture, cloud engineering, DevOps, security, and compliance teams to create trusted, governed, reusable, and AI-ready enterprise data assets that support reporting, analytics, machine learning, intelligent automation, robotic process automation (RPA), and generative AI solutions.

Responsibilities

How do I make an impact on my team ?

Serve as the technical lead for enterprise data engineering, lakehouse architecture, data modeling, and data platform modernization initiatives.
Design and implement lake house architectures utilizing Azure Databricks, Delta Lake, Azure Data Lake Storage Gen2, Unity Catalog, and related modern data platform technologies.
Develop canonical enterprise data models, business entity mappings, master data structures, and reusable data products supporting enterprise reporting, analytics, automation, and AI initiatives.
Build and maintain bronze, silver, and gold data layers supporting governed, trusted, and AI-ready datasets.
Develop and maintain metadata, data dictionaries, business glossaries, lineage documentation, and data catalog integrations supporting enterprise data governance.
Design, develop, and maintain ETL pipelines for structured and unstructured data across cloud and on-prem environments.
Build and optimize data models, schemas, and storage solutions in SQL Server, PostgreSQL, and cloud-native databases.
Design and deliver AI-ready data products supporting machine learning, generative AI, intelligent automation, retrieval-augmented generation (RAG), and agent-based AI solutions.
Support feature engineering, vectorization pipelines, document enrichment, semantic search, and knowledge management capabilities used by enterprise AI platforms.
Design and support healthcare data integration patterns including claims, eligibility, pharmacy, clinical, operational, financial, and third-party partner data.
Define and maintain enterprise data architecture standards, canonical data models, integration patterns, naming conventions, and data governance frameworks.
Lead enterprise data modeling efforts across operational, analytical, and AI workloads, ensuring consistent business definitions and semantic alignment across platforms.
Establish and govern enterprise data dictionaries, business glossaries, metadata standards, data lineage frameworks, and data stewardship practices.
Provide technical leadership and mentorship to Data Engineers, Data Integration Engineers, Analytics Engineers, and related technical resources.
Conduct architecture reviews for data platforms, integrations, analytics solutions, AI initiatives, and modernization programs.
Design enterprise data products and reusable domain-oriented datasets supporting reporting, analytics, automation, machine learning, and generative AI use cases.
Define reference architectures for healthcare data integration, interoperability, operational reporting, AI-ready datasets, and governed analytical platforms.
Support ingestion and transformation of structured, semi-structured, and unstructured healthcare datasets from internal and external sources.
Implement CI/CD workflows for data pipeline deployment and monitoring using tools such as GitHub Actions, Azure DevOps, or Jenkins.
Develop and maintain data integrations using AWS Glue, Azure Data Factory, Lambda, EventBridge, and other cloud-native services.
Design and implement DataOps practices including automated testing, deployment automation, data quality validation, monitoring, observability, and CI/CD pipelines for data workloads.
Develop API-based integrations supporting SaaS platforms, operational applications, third-party systems, healthcare data exchanges, intelligent automation platforms, and enterprise workflows.
Design and support event-driven architectures utilizing Event Hub, Event Grid, Service Bus, APIs, webhooks, and streaming data technologies.
Support machine learning, artificial intelligence, predictive analytics, and intelligent automation initiatives by developing scalable data pipelines, feature engineering datasets, training datasets, and operationalized data products.
Partner with RPA, automation, analytics, and AI teams to support workflow automation, intelligent document processing, agent-based AI solutions, and enterprise automation initiatives.
Implement secure data engineering practices including encryption, RBAC, data masking, row-level security, auditing, lineage tracking, and governance controls.
Ensure data quality, lineage, and governance through automated validation, logging, and monitoring frameworks.
Collaborate with cross-functional teams to gather requirements, design scalable solutions, and support analytics and reporting needs.
Monitor and troubleshoot data pipeline performance, latency, and failures; implement proactive alerting and remediation strategies.
Support data security and compliance by enforcing access controls, encryption standards, and audit logging aligned with HIPAA and SOC 2.
Maintain documentation for data flows, architecture diagrams, and operational procedures.
Participate in sprint planning, code reviews, and agile ceremonies to support iterative development and continuous improvement.
Evaluate and integrate new data tools, frameworks, and cloud services to enhance platform capabilities.
Partner with DevOps and Security teams to ensure infrastructure-as-code and secure deployment practices are followed.
Participate in, adhere to, and support compliance, people and culture, and learning programs.
Perform other duties as assigned.

Qualifications

What our team expect s from you?

Education: Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or related field, or equivalent work experience, required. Master’s degree preferred.
Certifications: AWS Certified Data Analytics or Solutions Architect, Microsoft Certified: Azure Data Engineer Associate, and Certified Data Management Professional (CDMP) required.
Experience:
8+ years of experience in Data Engineering, Data Architecture, Analytics Engineering, Data Platform Engineering, or related disciplines required.
5+ years of experience designing and implementing modern lake house architectures utilizing Azure Databricks, Delta Lake, Azure Data Lake Storage Gen2, Unity Catalog, and related cloud-native data technologies required.
Demonstrated experience leading enterprise data architecture, canonical data modeling, data governance, master data management, and large-scale data modernization initiatives required.
Experience designing enterprise data products, semantic models, business entity mappings, data dictionaries, and governed analytical datasets required.
Strong experience with Apache Spark, PySpark, SQL, Python, DataOps automation, CI/CD pipelines, and cloud-native data engineering practices required.
Experience supporting machine learning, AI, generative AI, intelligent automation, retrieval-augmented generation (RAG), vector-based architectures, and AI-ready data platforms preferred.
Experience supporting and modernizing legacy SQL-based ETL, reporting, data warehouse, and operational data environments required.
Experience planning and executing migrations from traditional database-centric architectures to cloud-native lakehouse, analytics, and AI platforms preferred.
Experience rationalizing legacy data assets, consolidating data pipelines, and establishing enterprise data architecture standards preferred.
Experience supporting healthcare data domains including claims, eligibility, pharmacy, clinical, operational, provider, financial, and regulatory data preferred.
Experience mentoring engineers, conducting architecture reviews, establishing engineering standards, and providing technical leadership across cross-functional teams preferred.
Knowledge of modern data architecture patterns including Data Mesh, Data Products, Medallion Architecture, Master Data Management, Event-Driven Architecture, and Lakehouse Governance preferred
Experience developing canonical data models, enterprise data products, data mappings, master data structures, and governed analytical datasets preferred.
Experience building DataOps pipelines, automated testing frameworks, CI/CD processes, and data quality controls preferred.
Experience supporting AI, machine learning, analytics, automation, and intelligent business solutions through scalable data engineering practices preferred.
Experience working within regulated environments supporting HIPAA, HITRUST, SOC 2, NIST, or similar compliance frameworks preferred.
Proven experience with SQL, ETL tools, and CI/CD pipelines.
Hands-on experience with AWS and Azure data services and infrastructure.
Experience with CI/CD pipelines, automated testing, and version control systems.
Skills & Technologies:
Data Platforms & Lakehouse: Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2 (ADLS), Databricks Workflows, Databricks Asset Bundles, Delta Live Tables (DLT), Synapse Analytics, Lakehouse Architecture, Data Products, Data Mesh Concepts.
Cloud & Platform Services: Azure Data Factory (ADF), Azure Functions, Azure Key Vault, Azure Monitor, Managed Identities, Private Endpoints, Event-Driven Architecture, Infrastructure Automation.
Data Engineering & Processing: Apache Spark, PySpark, SQL, Python, Data Modeling, Canonical Data Models, Master Data Management (MDM), Data Mapping, Data Transformation, ETL/ELT.
DataOps & Automation: Azure DevOps, GitHub Actions, CI/CD for Data Pipelines, Automated Testing, Data Quality Frameworks, Data Observability, Infrastructure as Code.
AI, Analytics & Machine Learning: Azure Machine Learning, Databricks ML, MLflow, Feature Engineering, AI-Ready Data Products, Predictive Analytics Support, Intelligent Automation, Robotic Process Automation (RPA).
Integration & APIs: REST APIs, GraphQL, Event Hub, Event Grid, Service Bus, Webhooks, Data Exchange Integration Patterns.
Languages & Tools: SQL, Python, Bash, Git, Terraform, PowerShell
ETL & Orchestration: AWS Glue, Azure Data Factory, Apache Airflow
CI/CD: GitHub Actions, Azure DevOps, Jenkins, DataOps Automation
Cloud Platforms: AWS (S3, Lambda, RDS, Redshift), Azure (Blob Storage, Synapse, Functions)
Data Platforms & Lakehouse: Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2 (ADLS), Synapse Analytics, Lakehouse Architecture, Data Products, Data Mesh Concepts
Security & Governance: Unity Catalog, RBAC, Data Lineage, Data Catalogs, Data Governance, HIPAA, HITRUST, SOC 2, Data Privacy Controls
Monitoring & Logging: CloudWatch, Azure Monitor, ELK Stack
Data Governance: Data cataloging, lineage tracking, encryption, and access control.