Job Description

Who You Are
You are an experienced and hands-on Data Engineering leader who combines strong technical expertise with a strategic mindset. You thrive on building data systems that are robust, efficient, and future-ready. You take ownership of end-to-end data architecture from ingestion through consumption, ensuring that the systems you build are trusted and high-performing. You are fluent in Python, PySpark, and SQL, and enjoy mentoring others, setting engineering standards, and collaborating across disciplines to drive data-driven decision-making.

Key Responsibilities

• Lead the design, development, and scaling of data infrastructure that powers analytics, machine learning, and business automation.

• Define and own data engineering best practices, including standards for data architecture & modeling, pipeline reliability, and quality assurance.

• Architect and oversee complex ETL/ELT workflows across cloud data platforms such as Azure, Databricks, Snowflake, dbt, and automated ingestion tools.

• Build and optimize production-grade data pipelines using Python, PySpark, and SQL.

• Partner with data science, analytics, and software engineering teams to design data systems that enable advanced analytics and model deployment.

• Mentor and coach data engineers, guiding career growth and technical excellence within the team.

• Evaluate and implement innovative tools and frameworks to enhance productivity, scalability, and observability across the data ecosystem.

• Lead and uphold enterprise data governance, including lineage tracking, master data management, privacy compliance, and security.

• Collaborate with business stakeholders to align data engineering initiatives with enterprise priorities and long-term platform strategy.

• Ensure data governance, privacy, and compliance are embedded throughout all data processes.

Required Qualifications

• 8+ years of experience in data engineering, data infrastructure, or related fields, including demonstrated leadership in architecting enterprise data solutions.

• Deep expertise in Python, PySpark, and SQL for large-scale data processing and transformation.

• Strong experience with cloud platforms (Azure, Snowflake, Databricks), including designing and optimizing cloud-native data pipelines.

• Advanced knowledge of dbt for data modeling and transformation and tools for automated data ingestion.

• Proven ability to design for reliability, performance, and cost optimization in large-scale data environments.

• Excellent communication skills with the ability to influence cross-functional partners and leadership.

Preferred Qualifications

• Experience leading data platform modernization or migration initiatives in cloud environments.

• Proficiency with real-time and event-driven architectures (e.g., Kafka, Kinesis).

• Familiarity with CI/CD practices for data pipelines and infrastructure-as-code (e.g., Terraform).

• Experience supporting machine learning workflows, model monitoring, and feature store management.

• Experience with data governance solutions (e.g., Purview, Collibra, Unity catalog).

• Background in regulated industries such as financial services or insurance.

Security and Privacy Responsibilities

• Follow organizational policies and procedures related to data security and privacy.

• Participate in ongoing training for data handling best practices.

• Treat all data with the highest standards of confidentiality.

• Report any security or privacy incidents promptly.