Staff Data Engineer, Later

• 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
• You’re proactive and results-driven, always taking initiative, aligning your actions with company goals, and delivering consistent outcomes
• Strategic and forward-thinking, you balance immediate needs with long-term opportunities to drive impactful, innovative results
• Your curiosity fuels success, keeping you sharp on industry trends, competition, and our cross-functional business dynamics
• Adaptable and resourceful, you handle shifting priorities with ease, manage your time effectively, and know when to ask for support
• You share insights to help the team stay ahead and make informed decisions
• You bring positivity and resilience to every challenge, tackling obstacles with grit and optimism that inspires those around you
• You lead with emotional intelligence, building trust, supporting others, encouraging growth, and fostering strong relationships through empathy and collaboration

Desirable

• As a Staff Data Engineer at Later, you will be at the forefront of designing and scaling our data platform, integrating multiple data sources, and ensuring high availability, efficiency, and security. You will drive architectural decisions, optimize large-scale data processing, and develop real-time analytics infrastructure.
• Partnering with cross-functional teams, including data science, product, and engineering, you will play a strategic role in shaping the company's data roadmap, implementing best practices, and influencing long-term data strategies. Your leadership and deep technical expertise will be essential in building a data-driven culture and empowering stakeholders with scalable and reliable data solutions
• 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