Design and implement scalable data platforms and pipelines across cloud environments
Develop reliable batch, streaming, and near-real-time pipelines
Build ingestion, transformation, and curation workflows for both structured and unstructured data
Implement modern data architectures including lakehouse patterns and medallion layering
Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
Design scalable logical and physical data models
Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents
Apply modern architecture patterns including event-driven and streaming architectures
Establish strong data observability
Enable data serving layers to support downstream systems
Collaborate with data scientists, ML engineers, analysts, and business stakeholders

Strong hands-on experience with Apache Spark and Delta Lake
Strong programming skills in Python and SQL
Proven experience building batch and streaming data pipelines
Solid understanding of data modeling, data quality, and governance principles.
Experience with one or more major cloud platforms (Microsoft Azure / Fabric preferred, also AWS or GCP)
Familiarity with modern data platforms (e.g., Databricks and Snowflake)
Experience with lakehouse architectures and distributed data systems
Strong understanding of scalability, reliability, and performance considerations in data pipelines
Problem-solving skills focused on scalability and reliability
Collaborative approach to working in cross-functional teams

Senior Data Engineer

Key skills