Job
Description
We are looking for a Senior Data Engineer to lead the design and implementation of scalable data infrastructure and engineering practices. This role will be critical in laying down the architectural foundations for advanced analytics and AI/ML use cases across global business units. Youll work closely with the Data Science Lead, Product Manager, and other cross-functional stakeholders to ensure data systems are robust, secure, and future-ready. Key Responsibilities: Architect and implement end-to-end data infrastructure including ingestion, transformation, storage, and access layers to support enterprise-scale analytics and machine learning. Define and enforce data engineering standards, design patterns, and best practices across the CoE. Lead the evaluation and selection of tools, frameworks, and platforms (cloud, open source, commercial) for scalable and secure data processing. Work with data scientists to enable efficient feature extraction, experimentation, and model deployment pipelines. Design for real-time and batch processing architectures , including support for streaming data and event-driven workflows. Own the data quality, lineage, and governance frameworks to ensure trust and traceability in data pipelines. Collaborate with central IT, data platform teams, and business units to align on data strategy, infrastructure, and integration patterns. Mentor and guide junior engineers as the team expands, creating a culture of high performance and engineering excellence. Qualifications: 10+ years of hands-on experience in data engineering, data architecture, or platform development. Strong expertise in building distributed data pipelines using tools like Spark, Kafka, Airflow, or equivalent orchestration frameworks. Deep understanding of data modeling, data lake/lakehouse architectures , and scalable data warehousing (e.g., Snowflake, BigQuery, Redshift). Advanced proficiency in Python and SQL , with working knowledge of Java or Scala preferred. Strong experience working on cloud-native data architectures (AWS, GCP, or Azure) including serverless, storage, and compute optimization. Proven experience in architecting ML/AI-ready data environments , supporting MLOps pipelines and production-grade data flows. Familiarity with DevOps practices, CI/CD for data , and infrastructure-as-code (e.g., Terraform) is a plus. Excellent problem-solving skills and the ability to communicate technical solutions to non-technical stakeholders.
