Staff Data Engineer

Illumina•San Diego, CA

About The Position

The Staff Data Engineer is a seasoned, hands-on engineer who designs, builds, and scales data products on our cloud lakehouse, powering analytics, reporting, and AI/ML across Illumina. We are looking for someone with strong proficiency in Python, SQL, and data modeling, a solid understanding of distributed systems and system design who has built and scaled data products on modern cloud platforms such as Databricks and Snowflake. This is a hands-on, senior individual-contributor role with end-to-end ownership and leadership spanning multiple domains such as Supply Chain, Manufacturing and Quality, including mentoring engineers on our global (India-based) team.

Requirements

10+ years of professional data engineering experience building and scaling data products on cloud platforms such as Databricks and/or Snowflake.
Strong proficiency in Python, including reusable framework development using functional and object-oriented programming.
Advanced SQL and strong data modeling skills (relational, dimensional, and lakehouse).
Solid understanding of distributed systems and system design for large-scale data processing.
Hands-on experience with open table formats (Delta Lake, and/or Apache Iceberg ) and big-data file formats (Parquet).
Experience with Spark and modern ELT tooling (e.g., dbt).
Experience with data observability , governance, security, and compliance practices (RBAC, PII, SOX).
Demonstrated adoption of AI in data and analytics engineering workflows.
Solid software engineering foundation — Git, REST APIs, JSON, CI/CD on at least one cloud environment (AWS preferred).
Strong written and verbal communication skills, with the ability to work effectively across business, AI, and platform teams and lead technical discussion.
Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, or a related field, or equivalent demonstrable experience.

Nice To Haves

Strong plus with domain knowledge of SAP, Manufacturing, and/or Quality data and processes.
Experience delivering in GxP / 21 CFR Part 11 or comparable regulated environments (life sciences, pharma, medical devices).
Experience with Unity Catalog and lakehouse governance at scale.
Snowflake-to-Databricks migration experience.
Exposure to SAP data (ECC / S/4HANA, CDS views) and SAP data integration patterns (e.g., SAP Business Data Cloud).
Bonus if candidate have additional domain knowledge such as Commercial and Finance.
Databricks and/or dbt certifications.
Familiarity with Power BI / Tableau and enabling BI and conversational-analytics.

Responsibilities

Partner across business, AI, and platform teams translating domain needs (e.g., SAP, Manufacturing, Quality) into well-modeled, governed and scalable data products.
Design, build, and scale end-to-end data products on Databricks (and interoperating with Snowflake) — from ingestion through curated, analytics-ready datasets following a medallion (Bronze/Silver/Gold) architecture.
Develop reusable frameworks, libraries, and standardized patterns in Python (functional and OOP as appropriate) for ingestion, transformation, validation, and publishing.
Design robust data models (relational, dimensional, and lakehouse) and apply strong system-design judgment to build performant, reliable distributed data pipelines using Spark, Delta Lake / open table formats, dbt, and SQL.
Embed data quality, reconciliation, validation, and governance into pipelines (Unity Catalog: lineage, RBAC, masking, PII handling.
Monitoring, alerting, troubleshooting, root-cause analysis, and SLA adherence for business-critical datasets.
Adopt AI in day-to-day data and analytics engineering to accelerate development, testing, and optimization.
Act as a technical leader — set standards, lead code reviews, contribute to architecture decisions, mentor engineers, and communicate trade-offs to peers and stakeholders.