102802 - Sr. Data Engineers

Summary

As a Senior Data Engineer you will design, build, and support the core systems that power our data platform, enabling fast, data-driven decisions. You will create scalable data pipelines, self-service tools, and governance solutions to ensure trusted, accessible data across the organization. This role works closely with business partners and the Data Platform team to support advanced analytics, including machine learning, and to elevate team capability through mentorship and knowledge sharing.

Responsibilities

• Design and build scalable data pipelines to ingest, transform, and curate data from APIs, databases, files, and event streams.
• Lead technical design reviews and translate complex business needs into enterprise-grade data solutions.
• Develop and optimize advanced data models (dimensional, data vault, domain-driven, canonical) to support analytics, BI, and productized datasets.
• Champion engineering excellence through software development lifecycle best practices, continuous delivery, and infrastructure automation using CI/CD and Infrastructure as Code.
• Optimize distributed workloads using SQL, Python, and Spark and mentor others on tuning techniques and scalable design patterns.
• Build reusable data frameworks, libraries, and reference architectures to accelerate team productivity and platform adoption.
• Perform root-cause analysis for major data incidents, lead long-term remediation, and drive operational reliability improvements.
• Provide technical mentorship to Data Engineers, conduct code reviews, and help shape engineering capability maturity.
• Collaborate with Architects, Data Leads, Product Owners, and cross-functional engineering teams to define long-term data strategies.
• Perform other duties as assigned.

Requirements

• 5 to 7+ years of experience in data engineering or a related technical field.
• Expertise in SQL and advanced proficiency in Python.
• Extensive hands-on experience building scalable pipelines and workflows in Databricks (Delta Lake, Spark, Unity Catalog, Jobs, Workflows).
• Hands-on experience with distributed data processing technologies such as Apache Spark.
• Proven experience designing and implementing complex data models across multiple business domains.
• Strong experience designing and tuning distributed data processing systems at scale.
• Strong knowledge of version control, CI/CD, DevOps/DataOps, and automated testing.
• Ability to lead cross-functional engineering initiatives and influence technical roadmaps.
• Strong problem-solving, debugging, and analytical skills in complex, multi-system environments.
• Proven experience with Azure cloud architecture for data engineering, including Azure Databricks integration, ADLS Gen2, Azure Data Factory (ADF), and Key Vault.
• Ability to thrive in agile, dynamic, and collaborative engineering teams.

Nice to Have

• Experience with Databricks Unity Catalog, Delta Live Tables, or Databricks Workflows.
• DataOps experience including pipeline observability, monitoring, and automated quality.
• Knowledge of metadata management or cataloging platforms such as Purview, Collibra, or Alation.
• Experience with streaming frameworks (Kafka, Event Hubs, Kinesis) used with Spark Structured Streaming.
• Knowledge and experience working in an Agile environment.