Staff Data Engineer

10+ years of experience in data engineering, software engineering, or related fields.
Proven experience leading the technical strategy and execution of large-scale data platforms.
Expertise in cloud technologies (Google Cloud Platform, AWS, Azure) with a focus on scalable data solutions (BigQuery, Snowflake, Redshift, etc.).
Strong proficiency in SQL, Python, and distributed data processing frameworks (Apache Spark, Flink, Beam, etc.).
Extensive experience with streaming data architectures using Kafka, Flink, Pub/Sub, Kinesis, or similar technologies.
Expertise in data modeling, schema design, indexing, partitioning, and performance tuning for analytical workloads, including data governance (security, access control, compliance: GDPR, CCPA, SOC 2)
Strong experience designing and optimizing scalable, fault-tolerant data pipelines using workflow orchestration tools like Airflow, Dagster, or Dataflow.
Ability to lead and influence engineering teams, drive cross-functional projects, and align stakeholders towards a common data vision.
Experience mentoring senior and mid-level data engineers to enhance team performance and skill development.

Lead the design and evolution of a scalable data architecture that meets analytical, machine learning, and operational needs.
Architect and optimize data pipelines for batch and real-time data processing, ensuring efficiency and reliability.
Implement best practices for distributed data processing, ensuring scalability, performance, and cost-effectiveness of data workflows.
Define and enforce data governance policies, implement automated validation checks, and establish monitoring frameworks to maintain data integrity.
Ensure data security and compliance with industry regulations by designing appropriate access controls, encryption mechanisms, and auditing processes.
Drive innovation in data engineering practices by researching and implementing new technologies, tools, and methodologies.
Work closely with data scientists, engineers, analysts, and business stakeholders to understand data requirements and deliver impactful solutions.
Develop reusable frameworks, libraries, and automation tools to improve efficiency, reliability, and maintainability of data infrastructure.
Guide and mentor data engineers, fostering a high-performing engineering culture through best practices, peer reviews, and knowledge sharing.
Establish and monitor SLAs for data pipelines, proactively identifying and mitigating risks to ensure high availability and reliability.

Experience with machine learning infrastructure and integrating ML models into data pipelines.
Experience with Kappa/Lambda architectures for real-time data processing.
Background in data observability, lineage tracking, and anomaly detection tools (Monte Carlo, Databand, Great Expectations, etc.).
Experience working with decentralized data architecture (e.g., Data Mesh principles).