ABOUT MOSER

For more than 25 years we have formed partnerships and grown through open and honest collaboration with our clients, partners, and employees. We are best known for taking great care of our clients, our dedication to creating a work environment where employees do their best work, and our deep commitment to continuous improvement. Our consultants work in a collaborative and fast-paced environment, are self-motivated, and are passionate about evolving technology. It is no accident that we are recognized as one of the Best Places to Work in Indiana for 10 consecutive years.
Internally, we believe in building strong teams from the top down with a focus on values in our Model-Coach-Care philosophy. Our leadership are encouraged and trained to model good practices, mentor other employees and each other, and show empathy and caring in all interactions. This is the base of our core values: Accountability, Balance, Collaboration, Focus, Integrity, Social Responsibility, Support and Transparency.
Moser Consulting believes in equal opportunity for all people and is committed to enabling a diverse, equitable, and inclusive culture. We foster a spirit of unity that respects the remarkable individuality of everyone’s culture, history, and service.

REQUIREMENTS

• Technical Expertise
• 5+ years of progressive experience in data engineering or analytics engineering roles with strong focus on data modeling.
• Advanced Python programming skills with expertise in data libraries (Pandas, NumPy, SQLAlchemy, PySpark, Snowflake Python Connector).
• Expert-level proficiency in SQL with experience across multiple database platforms (Snowflake, PostgreSQL, SQL Server, Oracle, MySQL).
• Advanced Snowflake expertise including SnowSQL, stored procedures, user-defined functions, and performance optimization.
• Strong experience with cloud data platforms (Azure and AWS) and their integration with Snowflake.
• Hands-on experience with data pipeline orchestration tools (Azure Data Factory, Airflow, DBT).
• Proficiency with version control systems (Git) and collaborative development workflows.
• Experience with containerization technologies (Docker, Kubernetes)
• Data Engineering Skills
• Expert-level understanding of data modeling concepts including dimensional modeling, normalization, denormalization, and data vault methodology.
• Advanced knowledge of slowly changing dimensions (SCD Types 1-7) and their implementation patterns.
• Deep knowledge of Snowflake architecture, micro-partitions, clustering keys, and query optimization.
• Knowledge of data warehouse concepts and modern data architecture patterns including data mesh and lakehouse architectures.
• Experience with both relational and NoSQL databases.
• Understanding of data governance, lineage, and quality management principles
• Consulting & Leadership
• Strong problem-solving skills with ability to debug complex data issues
• Experience with Agile/Scrum methodologies and project management practices.
• Excellent communication skills for technical and non-technical audiences.
• Ability to work independently and manage multiple client engagements simultaneously

PREFERRED SKILLS

• Bachelor’s degree in Computer Science, Data Engineering, or related technical field.
• Snowflake certifications (SnowPro Core, SnowPro Advanced Data Engineer).
• Cloud certifications (AWS Data Analytics, Azure Data Engineer, Google Cloud Data Engineer).
• Experience with additional programming languages (Scala, Java, R).
• Experience with data visualization tools (Tableau, Power BI, Looker).
• Background in specific industries (financial services, healthcare, retail, manufacturing).
• Previous consulting experience

• Technical Leadership & Architecture
• Design and architect end-to-end data pipelines, ETL/ELT processes, and data integration solutions.
• Lead comprehensive data modeling initiatives including dimensional modeling, star/snowflake schemas, and data vault methodologies.
• Develop advanced Snowflake solutions leveraging features like Time Travel, Zero Copy Cloning, and data sharing capabilities.
• Lead the development of complex SQL queries, stored procedures, and database optimization strategies.
• Build robust Python applications for data processing, automation, and analytics workflows.
• Architect cloud-native data platforms using Azure or AWS with Snowflake integration.
• Implement data quality frameworks and monitoring solutions.
• Design and optimize data warehouse and data lake architectures with focus on scalable data models
• Data Engineering Implementation
• Develop high-performance SQL code for data transformation and business logic implementation with emphasis on Snowflake-optimized patterns.
• Design sophisticated dimensional models leveraging star/snowflake schemas optimized for high-performance BI workloads across Power BI and Microsoft Fabric platforms.
• Experience developing and maintaining end-to-end ELT workflows within Azure Data Factory, Synapse Analytics, and Snowflake environments, ensuring optimal performance at enterprise scale.
• Design and develop APIs for data access and integration.
• Implement dimensional modeling best practices including slowly changing dimensions (SCD) and fact table optimization.
• Implement real-time streaming solutions using Kafka, Kinesis, or Snowflake Streams and Tasks
• Create comprehensive data documentation, lineage tracking, and data model documentation
• Client Engagement & Project Delivery
• Lead technical discovery sessions to understand client data landscape and requirements.
• Collaborate with business stakeholders to translate requirements into technical solutions.
• Mentor junior data engineers and guide technical decision-making.
• Conduct code reviews and establish development best practices.
• Provide technical expertise during pre-sales activities and proposal development.
• Performance Optimization & Governance
• Optimize database performance through indexing, partitioning, and query tuning.
• Implement data security and privacy controls in compliance with regulations (GDPR, HIPAA, SOX).
• Establish CI/CD pipelines for data engineering workflows.
• Monitor and troubleshoot production data systems