Senior Staff Engineer

Why this role is important to us: The staff engineer will work as a Data Architect will serve as a senior technical expert within the data engineering team, leading the design and implementation of highly scalable data pipelines, architectures, and frameworks. This role will focus on solving complex data challenges and ensuring that the company’s data infrastructure supports its business objectives. The staff engineer will also mentor other engineers and collaborate closely with cross-functional teams to ensure data systems are efficient, secure, and scalable. What will you do: 1. Data Architecture & Infrastructure: Design and build robust, scalable, and high-performance data architectures that enable seamless data access and analysis. Develop, optimize, and manage data pipelines and ETL processes for data ingestion from diverse data sources. Lead the design and implementation of cloud-based data warehousing and big data solutions (AWS, GCP, Azure). 2. Data Pipeline Optimization: Develop, maintain, and optimize data pipelines that efficiently move, transform, and process large volumes of structured and unstructured data. Implement best practices for data pipeline automation, ensuring real-time data availability and minimal downtime. 3. Advanced Data Solutions: Work on the integration of advanced data processing frameworks such as Spark, Kafka, and Hadoop for batch and real-time data streaming. Collaborate with data science teams to deploy machine learning models and pipelines into production. 4. Mentorship & Leadership: Act as a technical mentor to junior and mid-level data engineers, providing guidance on best practices, code reviews, and technical problem-solving. Lead by example in coding standards, technology decisions, and system architecture. 5. Collaboration: Work closely with data scientists, customer success, product teams, and other stakeholders to translate business needs into scalable data solutions. Ensure seamless data flow and availability for different teams to drive insights, reporting, and decision-making. 6. Performance & Security: Ensure data integrity, security, and compliance with company policies and industry standards. Optimize data storage, retrieval, and scalability to improve performance and reduce costs. 7. Innovation & Continuous Improvement: Stay up to date with emerging technologies and data engineering methodologies, continuously improving data infrastructure. Proactively propose innovative solutions to improve the efficiency and reliability of data systems. Education: Bachelor's or master's degree in computer science, Engineering, or related technical field. Experience Required: 12+ years of experience in data engineering or related fields. Strong experience in designing and building scalable data pipelines and architectures. Extensive hands-on experience with cloud platforms such as AWS, GCP, or Azure. Proven experience with big data technologies (e.g., Hadoop, Spark, Kafka, Hive). Strong experience with data warehousing solutions (e.g., Redshift, Snowflake, BigQuery, etc.) and data modeling. Must have designed and built at least two scalable systems. 1. Technical Skills: Proficiency in programming languages like Python, PySpark, or Scala. -Strong experience in designing robust APIs and microservices. o Experience with data pipeline orchestration tools (e.g., Apache Airflow, Kubeflow, etc.) -Deep understanding of ETL/ELT processes and data modeling. 2. Problem Solving & Innovation: o Strong analytical skills with the ability to solve complex technical challenges. -Experience in troubleshooting data systems and optimizing their performance. -Ability to evaluate and recommend new technologies and tools to improve data systems. 3. Collaboration & Communication: -Excellent communication skills, with the ability to explain complex technical issues to both technical and non-technical stakeholders. -Ability to work in a collaborative, cross-functional environment with multiple stakeholders. -Strong interpersonal skills and experience mentoring other engineers. Good To Have: Hands-on experience with machine learning pipelines and working with data science teams. Experience with data governance frameworks and compliance (e.g., GDPR, CCPA). Experience in real-time data streaming and processing technologies (e.g., Kafka, Kinesis, Flink). Good knowledge of SQL and database technologies. Familiarity with containerization (e.g., Docker) and orchestration (e.g., Kubernetes) is a plus. Experience building data pipelines for CPG/Retail industries Work Environment: Fast-paced, dynamic, and collaborative work environment. Opportunities to work with cutting-edge technologies and continuously grow in your career.