Senior Data Engineer (34454)

KLS MARTIN LP•Jacksonville, FL

1d•Onsite

About The Position

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.

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

Nice To Haves

Experience with Microsoft Fabric/Synapse/OneLake preferred.
Experience with understanding data requirements and performing data preparation and feature engineering tasks to support model training and evaluation preferred.

Responsibilities

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.
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.
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.
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.).
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.
Architect and implement cloud-based data warehouse solutions (e.g., Snowflake, AWS Redshift, Google BigQuery, Azure Synapse).
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.
Establish monitoring solutions for data pipeline failures, schema changes, and data anomalies.
Set up logging and alerting mechanisms to proactively identify and resolve issues.
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.
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.