Senior Data Engineer

KLS Martin Group; Jacksonville (FL); On-site; USD 110,000 - 170,000; Full time

Benefits offered by this job

Paid parental leave

In-house training

Culture of innovation

Job summary

KLS Martin Group seeks an experienced data warehouse architect/engineer to lead scalable data warehouse design and ETL/ELT development. You will build robust data models, ensure data quality, and govern data across AWS/GCP/Azure platforms while collaborating with cross-functional teams to enable analytics and data-driven decisions.

The role requires 6+ years in data analytics/engineering, strong SQL, and hands-on experience with cloud data warehouses and BI tools.

Qualifications

• Bachelor’s degree with 6+ years in data analytics, data engineering, or data architecture.
• Experience designing dimensional models (star/snowflake) for enterprise warehouses.
• Hands-on with ETL/ELT development using listed tools.
• 3+ years in cloud data warehouse platforms (Snowflake, Redshift, BigQuery, Synapse).
• Strong SQL, Python/Scala for processing and transformation.
• Experience with relational and NoSQL databases; familiarity with RBAC, governance.

Responsibilities

• Lead end-to-end data warehouse design and implementation for a scalable, high-performance solution.
• Develop dimensional models and robust data architectures for analytics and BI.
• Design, develop, and optimize ETL/ELT pipelines across sources.
• Establish data governance, quality, and security practices including RBAC and metadata management.
• Tune performance via indexing, partitioning, caching, and query optimization.
• Collaborate with stakeholders, engineers, and DevOps for reliable data infrastructure.
• Mentor team members and define standards for development and governance.
• Support data science/ML efforts with data prep and feature engineering.

Skills

SQL

Data modeling

ETL/ELT

Cloud platforms

Python

Dimensional modeling

Education

Bachelor’s degree in a related field

Tools

dbt

Informatica

Talend

Apache Airflow

Snowflake

AWS Redshift

Google BigQuery

Azure Synapse Analytics

Tableau

Looker

Power BI

Mode Analytics

Job description

Description

Who We Are

At KLS Martin, we offer a unique opportunity to contribute to the success of a dynamic and thriving company whose products are used daily across the world to help surgical patients.

KLS Martin Guiding Principles

• Established, Privately Held Business Group – Responsive to customers, not shareholders. KLS Martin has manufactured medical products since 1896, and we have sold our products in the United States under the KLS name since 1993. We have always been, and always will be, privately owned.
• Patient Focus – We design products with the patient in mind – CMF, Thoracic & Hand
• Product to Table – Integrated planning, design, manufacturing and distribution process
• Educational Partner – Our primary focus for support is on education
• Inventory Alliance – Inventory management is critical to patient treatment/outcome
• Surgical Innovation is Our Passion – More than just a tagline

What We Offer

• We provide full-time employees with a competitive benefits package, including paid parental leave
• In-house training and professional development opportunities
• A culture of creativity and innovation by drawing on diverse perspectives and ideas to drive surgical innovation

Job Summary

This position will lead the design, evaluation, implementation, and testing of a scalable, high-performance enterprise data warehouse. In this role, you will be responsible for developing dimensional data models, ETL/ELT pipelines, and cloud-based data solutions that enable robust analytics and reporting. You will collaborate with cross-functional teams to ensure data quality, governance, and security while optimizing performance for large-scale datasets. The ideal candidate has a strong background in SQL, cloud data platforms (AWS, GCP, or Azure), and data pipeline automation and is enthusiastic about building modern data architectures and enabling data-driven decision-making.

Responsibilities

Essential Functions, Duties, and Responsibilities

• Data Warehouse Design & Architecture
  • Lead the end-to-end design and implementation of a scalable, high-performance data warehouse.
  • Define data architecture principles, strategies, and best practices to ensure optimal performance and maintainability.
• Dimensional Modeling & Data Modeling
  • Design and implement robust dimensional models (star and snowflake schemas) to optimize analytical queries.
  • Develop conceptual, logical, and physical data models to support reporting, analytics, and business intelligence (BI).
  • Ensure data models align with business requirements and support self-service analytics.
  • Leverages existing data infrastructure to fulfill all data-related requests, perform necessary data housekeeping, data cleansing, normalization, hashing, and implementation of required data model changes.
• ETL/ELT Development & Data Pipelines
  • Design, develop, and optimize ETL/ELT pipelines to ingest, transform, and load structured and unstructured data from various sources.
  • Ensure data pipelines are scalable, efficient, and maintainable while handling large datasets.
  • Implement incremental data processing strategies to manage real-time and batch workloads.
• Data Governance, Quality, and Security
  • Establish data governance practices, including data cataloging, lineage tracking, and metadata management.
  • Implement data validation, anomaly detection, and quality monitoring to ensure accuracy and consistency.
  • Collaborate with security teams to enforce role-based access control (RBAC), encryption, and compliance standards (GDPR, HIPAA, etc.).
• Performance Tuning & Optimization
  • Optimize database performance by indexing, partitioning, caching, and query tuning.
  • Monitor and troubleshoot slow queries, ensuring efficient use of resources.
  • Analyze data to spot anomalies, trends and correlate similar data sets.
• Cloud Technologies
  • Architect and implement cloud-based data warehouse solutions (e.g., Snowflake, AWS Redshift, Google BigQuery, Azure Synapse).
• Collaboration & Cross-Functional Work
  • Work closely with business stakeholders & analysts to translate requirements into scalable data solutions.
  • Partner with software engineers and DevOps teams to ensure seamless data integration and infrastructure reliability.
• Monitoring & Incident Response
  • Establish monitoring solutions for data pipeline failures, schema changes, and data anomalies.
  • Set up logging and alerting mechanisms to proactively identify and resolve issues.
• Mentorship & Best Practices
  • Guide and mentor other members of the data team on best practices in data modeling, ETL, and architecture.
  • Define and document standards for data warehouse development, maintenance, and governance.
• Data Science & Machine Learning
  • Designs, develops, and implements statistical models to carry out various novel aspects of classification and information extraction from data.
  • Designs, develops, and implements natural language processing software modules.
    

Qualifications

Educational and Experience Requirements

• Bachelor’s degree and 6+ years of experience in data analytics, data engineering, data architecture, or software engineering with a focus on data warehouse design and implementation.
• Proven experience designing and implementing dimensional data models (star schema, snowflake schema) for enterprise-scale data warehouses.
• Hands-on experience with ETL/ELT development using tools like dbt, Informatica, Talend, Apache Airflow, or custom data pipelines.
• 3+ years of experience working with cloud-based data warehouse platforms such as Snowflake, AWS Redshift, Google BigQuery, or Azure Synapse Analytics.
• Strong knowledge of SQL, Python, and/or Scala for data processing and transformation.
• Experience with relational and (preferably) NoSQL databases (e.g., SQL Server, MySQL, MongoDB, Cassandra).
• Experience with Microsoft Fabric/Synapse/OneLake preferred.
• Experience implementing data governance, data quality frameworks, and role-based access control (RBAC).
• Experience w orking closely with other departments and teams to ensure alignment on goals and objectives for successful project execution.
• Perform work under minimal supervision.
  

Experience with understanding data requirements and performing data preparation and feature engineering tasks to support model training and evaluation preferred.

Knowledge, Skills, And Abilities

• Identify areas of improvement, troubleshoot issues, and propose creative solutions that drive efficiency and better business results.
• Continuously assess and refine internal processes to enhance team productivity and reduce bottlenecks.
• Able to assess complex problems, weigh options, and devise practical solutions.
• Handle complex issues and problems and refers only the most complex issues to higher-level staff.
• Deep understanding of dimensional modeling (star and snowflake schemas), OLAP vs. OLTP, and modern data warehouse design principles.
• In-depth knowledge of ETL/ELT best practices, incremental data processing, and tools like dbt, Apache Airflow, Informatica, Talend, Fivetran.
• Understanding of data governance frameworks, data lineage, metadata management, role-based access control (RBAC), and compliance regulations (GDPR, HIPAA, CCPA).
• Knowledge of query tuning, indexing, partitioning, caching strategies, and workload optimization for large-scale data warehouses.
• Regularly update and communicate project status, timelines, and risks to both internal and external stakeholders.
• Ability to write optimized, complex SQL queries for data retrieval, transformation, and aggregation.
• Hands-on experience building scalable, fault-tolerant data pipelines for batch and real-time processing.
• Experience with RESTful APIs, event-driven architecture (Kafka, Pub/Sub), and integrating third-party data sources.
• Practical experience deploying and managing data solutions on AWS (S3, Glue, Lambda), GCP (Dataflow, Pub/Sub), or Azure (Data Factory, Synapse Analytics).
• Familiarity with BI tools (Tableau, Looker, Power BI, Mode Analytics) and self-service analytics enablement.
• Design and implement high-performance, resilient, and cost-effective data architectures.
• Analyze and resolve data integrity, scalability, and pipeline failures with structured problem-solving.
• Provide leadership, coaching, and/or mentoring to foster growth in the team.
• Explain technical concepts to non-technical stakeholders and advocate for data-driven decision-making.
• Stay current with evolving data engineering trends, tools, and methodologies.

Skill Requirements

• Typing/computer keyboard
• Utilize computer software (specified above)
• Retrieve and compile information
• Verify data and information
• Organize and prioritize information/tasks
• Advanced mathematical concepts (fractions, decimals, ratios, percentages, graphs)
• Verbal communication
• Written communication
• Research, analyze and interpret information
• Investigate, evaluate, recommend action
• Basic mathematical concepts (e.g. add, subtract)
• Abstract mathematical concepts (interpolation, inference, frequency, reliability, formulas, equations, statistics)

Physical Requirements

• Sitting for extended periods
• Extended periods viewing computer screen
• Reading
• Speaking
• Hear/Listen
• Maintain regular, punctual attendance
• Pushing/Pulling
• Bending/Stooping
• Reaching/Grasping
• Writing

Hazards

• Normal office environment

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

KLS Martin is a drug-free employer