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