Senior Data Engineer

We're seeking a Senior Data Engineer (EDW) to design, build, and optimize enterprise-grade data pipelines and dimensional models that power analytics and BI at scale. You will apply dimensional modeling to curate trustworthy subject areas, build resilient ingestion and transformation pipelines in Databricks and IBM DataStage, and deliver governed, performant datasets in Snowflake. This role partners closely with Data Architecture, Data Analysts, BI Developers, and SRE to ensure data is correct, timely, secure, and discoverable.

Key Responsibilities

Data Modeling & ETLBuild Kimball-style dimensional models (conformed dimensions, fact tables, aggregates) to support enterprise analytics.
Curate data from source systems into raw → refined → curated EDW layers, applying business transformations, standardization, and harmonization rules.
Implement SCD Type 1/Type 2 patterns, surrogate key management, late-arriving dimension/fact logic, and historical tracking strategies.
Develop and optimize SQL queries for largescale analytical workloads.
Optimize Snowflake curated tables (partition/micropartition awareness, clustering, caching patterns).
Data Quality & ValidationBuild embedded data quality checks within transformation pipelines (null checks, referential integrity, reconciliation, duplicates, SCD integrity, etc.).
Perform detailed reconciliation between EDW curated layers and upstream systems.
Drive defect prevention through strong analysis, testability, and peer review.
Metadata, Lineage & DocumentationMaintain technical specs, mapping sheets, and data lineage from sources through curated layers.
Ensure curated assets meet EDW standards and are discoverable by analysts, BI developers, and stakeholders.
Collaboration & LeadershipWork with Data Architects on standards and reference patterns; mentor engineers on best practices.
Collaborate with Analysts/BI Developers to align delivery with business needs; support stakeholder demos and release communications.
Contribute to documentation (Confluence/JIRA/etc.), data catalogs, and onboarding materials.
Required Qualifications7+ years in data engineering with focus on DW/ETL
Experience with ETL/ELT tools and platforms (e.g. Azure Databricks, Datastage, Apache Spark, Azure Data Factory, Snowflake).
Snowflake—SQL, performance tuning, tasks/streams, time travel/cloning, RBAC, security/masking, cost optimization.
Advanced SQL skills, including complex joins, window functions, and performance optimization.
Solid understanding of data warehousing and dimensional modeling concepts.
Practical exposure to data quality testing, validation, or analytics governance frameworks.
Demonstrated experience with cloud data platforms (Azure preferred)
Familiarity with version control and collaborative development workflows (e.g., Git).
Comprehensive understanding of software development lifecycle and data warehousing concepts
Preferred / Nice-to-Have SkillsExperience with SAP and IBM DataStage is also beneficial.
Familiarity with semantic modeling and BI concepts
Knowledge of data governance principles, data security best practices, and compliance requirements in healthcare.

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