Principal Data Engineer

Duties & Responsibilities

Define and Drive Enterprise Data Strategy

Develop and own the strategic architecture for enterprise-scale data platforms, pipelines, and ecosystems, ensuring alignment with business objectives and long-term scalability.

Architect High-Performance Data Solutions

Lead the design and implementation of robust, large-scale data solutions across multi-cloud environments (AWS, Azure, GCP), optimizing for performance, reliability, and cost efficiency.

Establish Engineering Excellence

Create and enforce best practices, coding standards, and architectural frameworks for data engineering teams, fostering a culture of quality, automation, and continuous improvement.

Provide Technical Leadership and Mentorship

Act as a trusted advisor and mentor to engineers across multiple teams and levels, guiding technical decisions, career development, and knowledge sharing.

Align Data Strategy with Business Goals

Partner with senior business and technology stakeholders to translate organizational objectives into actionable data strategies, ensuring measurable business impact.

Champion Modern Data Stack Adoption

Drive the adoption and integration of cutting-edge technologies such as Snowflake, DBT, Airflow, Kafka, Spark, and other orchestration and streaming tools to modernize data infrastructure.

Architect and Optimize Data Ecosystems

Design and manage enterprise-grade data warehouses, data lakes, and real-time streaming pipelines, ensuring scalability, security, and high availability.

Implement Enterprise Testing and Observability

Establish rigorous testing, validation, monitoring, and observability frameworks to guarantee data integrity, reliability, and compliance across all environments.

Ensure Governance and Compliance

Oversee data governance initiatives, including lineage tracking, security protocols, regulatory compliance, and privacy standards across platforms.

Enable Cross-Functional Collaboration

Work closely with analytics, data science, and product teams to deliver trusted, business-ready data that accelerates insights and decision-making.

Provide Thought Leadership

Stay ahead of emerging technologies and industry trends, influencing enterprise data strategy and advocating for innovative solutions that drive competitive advantage.

Lead Enterprise-Scale Deployments

Oversee large-scale data platform deployments, ensuring operational excellence, scalability, and cost optimization for global business needs.

Requirements

Basic Qualifications

Bachelors degree in Computer Science, Information Systems, Engineering, or a related field (Masters or equivalent advanced degree preferred)

1115 years of progressive experience in data engineering or related fields

Deep expertise in SQL, data warehousing, and large-scale data modeling

Minimum 5 years of experience with Snowflake or another modern cloud data warehouse at scale

Minimum 3 years of experience with Python or equivalent programming languages for ETL/ELT and automation

Proven experience architecting data platforms on major cloud providers (AWS, Azure, or GCP)

Hands-on experience with orchestration tools (Airflow, ADF, Luigi, etc.) and data transformation frameworks (DBT)

Strong track record in designing and implementing large-scale data pipelines and solutions

Demonstrated experience leading cross-functional teams and mentoring senior engineers

Excellent communication skills for engaging with business stakeholders and cross-functional partners

Preferred Qualifications

Expertise in Real-Time Data Processing

Hands-on experience with streaming platforms such as Apache Kafka, Spark Streaming, and Apache Flink, enabling low-latency, high-throughput data pipelines for real-time analytics.

Strong DevOps and CI/CD Practices

Deep understanding of DevOps principles, automated CI/CD pipelines, and Git-based workflows tailored for data engineering environments, ensuring rapid, reliable deployments.

Domain Knowledge in Retail and E-Commerce

Proven experience working with customer-centric data ecosystems, leveraging data to drive personalization, operational efficiency, and business growth in retail or e-commerce contexts.

Track Record of Driving Innovation

Recognized for introducing technical innovations, improving engineering processes, and advancing organizational data maturity through strategic initiatives.

Experience in ML Ops and AI/ML Lifecycle

Practical knowledge of ML Ops frameworks, including building data pipelines for model training, managing feature stores, and implementing monitoring solutions for AI/ML models in production.

Strategic Alignment and Leadership

Ability to understand and interpret organizational vision and decision-making frameworks, aligning team objectives and personal goals to deliver measurable business impact.

Technology Evangelism and Trend Awareness

Up-to-date with emerging technologies, industry best practices, and modern data architectures; consistently brings innovative ideas and thought leadership to the team.