Senior Data Engineer

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.

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 Client 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 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.

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