Senior Technical Lead

Job Summary

Polaris is seeking a highly skilled Senior Data Engineer to design, build, and optimize scalable data solutions that power analytics, reporting, AI, and ML use cases. This role requires deep expertise in data pipeline development, data quality, testing, modern data platforms, and performance optimization.

You will play a critical role in building out our Enterprise Data Warehouse (EDW) and Metric Store products, ensuring high-quality and reliable data pipelines, and enabling business insights through well-structured semantic and analytical layers.

The Data & Analytics (D&A) team encompasses Data Engineering, Business Intelligence, Data Science, Master Data Management, and AI. The Sr. Data Engineer will support these teams, as well as federated data roles, by developing data platforms (warehouses, lakes, semantic models) to provide high quality, easy to use data sets and metadata models. This role reports to the Manager, Data Engineering.

A successful candidate has a deep data engineering background, experience supporting analytics projects, is able to work with technical and non-technical audiences, has a bias for action, understands optimization techniques, and focuses on quality by design

Key Responsibilities

Data Engineering & Pipeline Development •           Design, develop, and maintain scalable ETL/ELT data pipelines across batch and streaming workloads •           Implement robust data ingestion frameworks using high watermark logic, CDC strategies, and incremental processing •           Build and manage data pipelines with orchestration tools (e.g., Azure Data Factory, Fabric, Fivetran, etc.) •           Ensure reliable end-to-end data movement, transformation, and integration across systems Data Quality & Reliability •           Define and enforce data quality standards, rules, and validation frameworks •           Implement automated data quality monitoring, anomaly detection, and alerting •           Build reusable testing frameworks for data validation and reconciliation •           Partner with stakeholders to ensure data accuracy, completeness, and consistency Data Modeling & Architecture •           Develop and implement scalable dimensional data models (star/snowflake schemas) •           Apply medallion architecture principles (Bronze/Silver/Gold) for structured data refinement •           Develop and maintain semantic layers to support BI tools such as Power BI •           Ensure alignment between raw data, curated datasets, and business-facing models Performance Optimization •           Tune and optimize data pipelines and queries for cost and performance efficiency •           Implement partitioning, clustering, cold storage, and indexing strategies within platforms like Snowflake and Azure •           Monitor pipeline performance and optimize resource utilization across cloud environments Tools & Platform Development •           Leverage modern data platforms: Snowflake, Microsoft Azure (Data Factory, Synapse, Data Lake, Fabric), Power BI, Fivetran, other ETL/ELT tools and orchestration frameworks •           Build reusable frameworks and accelerators to standardize development practices CI/CD, Testing, and Automation •           Develop and maintain CI/CD pipelines for data engineering workflows •           Implement unit testing, integration testing, and regression testing for pipelines •           Ensure version control and proper promotion across environments (dev/stage/prod) Documentation & Governance Standards Maintain comprehensive documentation for pipelines, architectures, and data models Define and enforce coding standards, naming conventions, and development guidelines – mentor j

Skill Requirements

• 8+ years of experience in Data Engineering or related field
• Strong expertise in SQL and data transformation frameworks
• Hands-on experience with: Snowflake, Azure (ADF, Synapse, Data Lake, Fabric), Power BI or similar BI tools
• Proven experience building and optimizing ETL/ELT pipelines
• Deep understanding of dimensional modeling, medallion architecture, and semantic layer design
• Experience with data orchestration tools and scheduling frameworks
• Strong knowledge of incremental processing, high watermark logic, and CDC patterns
• Experience leveraging AI tools for development or analytics workflows
• Knowledge of data governance, lineage, and metadata management practices
• Experience with CI/CD tools (Azure DevOps, GitHub, etc.)
• Background in data quality frameworks and testing methodologies

Familiarity with Python or Spark-based processing

Other Requirements

• Corporate office environment – fast-paced

• Operate with minimal supervision