Senior Engineer - Data Engineering

Location: Sysco LABS – Hybrid

This position is with the Cyber Analytics & Business Intelligence program at Sysco where you’ll play a key role integrating data end-to-end for a complete lifecycle view. Your primary responsibility will be engineering AI-enabled Business Intelligence solutions for Cyber Security programs.

By utilizing the underlying data from diverse security tools and technologies, you will build actionable dashboards and AI-ready data models. These solutions will help cyber security teams identify existing and emerging threats, highlight operational gaps, and empower business stakeholders to make timely decisions, proactively mitigate risks, and invest wisely. You will be instrumental in transforming data into the structured foundations required for predictive analytics and Generative AI (GenAI) enablement.

• →AI-Enabled Data Pipeline Development: Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and load massive volumes of cyber security data (SIEMs, EDRs, network logs, cloud logs, vulnerability scanners) using a DevSecOps mindset. Utilize modern cloud orchestration and container tools (e.g., AWS ECS, Azure Data Factory/Container Apps, or optionally GCP Cloud Composer/Run), ensuring pipelines are optimized to feed both BI tools and AI/ML model training.
• →Advanced Data Modeling & AI Prep: Develop, optimize, and manage highly efficient data models in enterprise cloud data warehouses (e.g., Snowflake, AWS Redshift, Azure Synapse/Fabric, or Google BigQuery). Design feature stores, semantic layers, and vector-ready datasets that ensure data integrity, performance, and accessibility for both traditional reporting and GenAI (e.g., RAG) integrations.
• →Cloud-Native ETL Solutions: Leverage cloud services—including serverless functions and orchestration workflows—for robust data processing, automation, and comprehensive monitoring across AWS or Azure (GCP experience is a plus).
• →Power BI / Looker Reporting & AI Integration: Ensure data models are highly optimized for consumption by Power BI (or Looker). Collaborate with stakeholders to build compelling dashboards that visualize key security metrics and integrate AI driven forecasting and anomaly indicators.
• →AI Strategy & Enablement: Partner with cross-functional teams to integrate foundational AI concepts into the data architecture, paving the way for LLM adoption, automated summaries, and intelligent alerting.
• →Data Quality & Governance: Implement robust data quality checks, monitoring, and alerting mechanisms to ensure the accuracy and reliability of security data. Contribute to governance policies critical for trusted AI outputs.
• →Performance Optimization: Continuously monitor and tune the performance of data pipelines, queries, and warehouse models to ensure efficient processing and low-latency insights.
• →Collaboration & Documentation: Work closely with Cyber Security Analysts, Security Operations, and engineering teams to capture data/AI requirements. Create and maintain thorough documentation for pipelines, models, and architectures.
• →Innovation: Stay current with the latest trends in data engineering, multi-cloud capabilities (AWS/Azure / GCP preferred), and AI/ML data practices to drive continuous innovation within the cyber security domain.

• Experience: 3 - 5+ years of experience as a Data Engineer, with a strong focus on building data pipelines, data warehousing, and preparing data for advanced analytics.
• Cloud Data Warehousing: Deep hands-on experience with modern cloud data platforms such as Snowflake, AWS Redshift, or Azure Synapse (Google BigQuery experience is good to have), including advanced data modeling, query optimization, and data migration/re-platforming frameworks.
• Cloud Platform Expertise (AWS or Azure Required; GCP is optional):
• Hands-on experience with serverless functions and cloud orchestration (e.g., AWS Lambda, ECS/Fargate, Step Functions, Azure Functions, Container Apps, Data Factory).
• Familiarity with broader cloud data ecosystems (e.g., AWS S3, Pub/Sub via Kinesis/MSK, EMR/Databricks OR Azure Blob Storage, Event Hubs, Databricks).
• AI/ML Exposure: Practical exposure to AI concepts, platforms, and data engineering practices that support Machine Learning (e.g., AWS SageMaker, Azure AI Services, Vertex AI, embedding generation, setting up pipelines for LLMs).
• Programming: Strong proficiency in Python for data manipulation, API integration, and automation.
• Data Architecture: Strong understanding of ETL/ELT principles, data warehousing concepts, and dimensional data modeling.
• BI Tooling: Knowledge of building data solutions specifically optimized for Power BI (Looker expertise is a plus).
• Process Orientation: Strict adherence to engineering standards, agile methodologies, and DevSecOps best practices.
• Communication: Excellent ability to read/comprehend specifications, produce clear documentation, and articulate technical roadblocks or architectural decisions in meetings.

• 1+ Years hands on Experience with GCP platform and related technologies (BigQuery , Manged Airflow, Cloud Functions, GCS etc )
• Cyber Security Domain: Experience working with security-related data, such as SIEM logs, network flows, or vulnerability management platforms.
• DevOps Tooling: Hands-on experience with fundamental CI/CD and infrastructure as-code tools (e.g., Terraform, GitHub Actions).
• Governance: Understanding of data governance frameworks and strict security protocols for handling sensitive data.
• Data Science Interest: A strong interest or background in data science, predictive modeling, or statistical analysis.