Data Engineer - III - Databrciks

A key focus of this role is accelerating feature store expansion through robust data modeling, pipeline development, and feature engineering, while contributing to pricing semantic and ontology frameworks that drive consistency across the data ecosystem. Working closely with Data Scientists, analysts, and business stakeholders, this role ensures that high-quality, reliable data is accessible across the organization to support pricing optimization, model experimentation, and strategic decision-making.

Senior Data Engineer
Overview
The Data Engineer designs, builds, and maintains scalable data pipelines and infrastructure that power analytics, machine learning, and business intelligence initiatives. Working closely with Data Scientists, analysts, and business stakeholders, this role ensures that high-quality, reliable data is accessible across the organization to support pricing optimization, model experimentation, and strategic decision-making. A key focus of this role is accelerating feature store expansion through robust data modeling, pipeline development, and feature engineering, while contributing to pricing semantic and ontology frameworks that drive consistency across the data ecosystem.
Duties and Responsibilities
Design, build, and maintain robust real-time and batch data pipelines to ingest, transform, and deliver structured, semi-structured, and unstructured data from multiple sources
Develop and manage metadata-driven, real-time streaming pipelines and distributed data processing workflows at scale
Build and expand the feature store through data modeling, pipeline development, feature building, and data transforms that accelerate ML model readiness
Contribute to the design and build of pricing semantic layers, ontologies, and knowledge graphs to ensure consistent, reusable data definitions across the organization
Support Data Marketplace development, enabling teams to discover, access, and share data products across the organization
Develop cognitive search engine capabilities and support Data Syndication to distribute data across internal and external consumers
Build and expand the feature store through data modeling, pipeline development, feature building, and data transforms
Build data applications and support writeback capabilities that enable business users to interact with and act on data directly
Optimize database performance and query efficiency to support large-scale analytics workloads
Partner with cross-functional teams to understand data needs and translate them into scalable engineering solutions
Minimum Qualifications/Requirements
Bachelor's degree in Computer Science, Engineering, or related field
5+ years of experience in data engineering, ETL development, or a related role
Proficiency in SQL and Python, including Pandas, PySpark, and Python functions
Experience with cloud data platforms (e.g., Databricks Unity Catalog) and pipeline orchestration tools (e.g. dbt)
Demonstrated experience with data modeling, feature engineering, and building transforms for ML feature stores
Strong understanding of data modeling, warehousing concepts, and relational databases
Preferred Skills
Experience with data quality frameworks such as Great Expectations and Databricks Delta Sharing
Working knowledge of Semantic Engineering, flexible data modeling, and ontology design (OWL, knowledge graphs)
Familiarity with Data Governance frameworks, Master Data Management practices, and Data Migration methodologies
Experience building pricing semantic layers or data ontologies to standardize business definitions across teams
App building skills are a strong plus — experience developing data apps, dashboards, or writeback solutions (e.g., Streamlit, Hex, or similar tools)
Experience supporting ML pipelines and working alongside Data Science teams
Knowledge of DevOps practices including CI/CD and version control (Git)
Strong communication skills and ability to work across technical and business teams
Attention to detail and commitment to data accuracy and integrity

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