LTK is experiencing explosive growth with exciting innovation in our roadmaps – driven by data and technology. As an Engineering Manager, Time Series and Data Streaming , you will be a technical leader within a growth-hungry, acceleration-minded team of problem-solvers and product-builders. We thrive in an extremely dynamic competitive environment focusing relentlessly on the needs of our different customers (brands, creators, and consumers). We seek thought-partners who can collaborate effectively with engineering, product, and marketing teams to deliver quality solutions rapidly.

We are looking for a hands-on data engineering manager who will lead the squad that will stand up our event streaming and real-time analytics framework.  This is part of the continued evolution of the LTK Data Foundation and Data Engineering capability that delivers robust, reliable, and usable data pipelines and data sets to fuel our products, analytics, and data science solutions.The ideal candidate would also have depth in understanding time series based solutions as we consume events across first party and third party platforms.

Responsibilities

• Lead the squad responsible for the technical definition, prototyping, and production-ready enablement of LTK’s event streaming and real-time analytics framework, optimizing efficiencies, scale, and cost
• Manage all aspects of agile ceremonies (daily scrum, backlog grooming, sprint planning, retros, cross-squad alignment, etc.)
• Cultivate a high performing engineering team that delivers on-time, high quality, and highly reusable work products
• Build and attract data engineering talent
• Participate in the overall strategic direction of the data engineering technology stack
• Stay abreast of data engineering technical trends and emerging approaches
• Participate in building the data engineering roadmaps and plans
• Define technical projects (“epics”) with clarity including scope, technical approach, and work estimations
• Effectively communicate and gain alignment both within team and with interdependent teams and stakeholders
• Produce high quality technical documentation and artifacts that are easily consumable by data teams and other engineering teams

Qualifications

• Bachelor’s or Master’s degree in information systems, computer science, data engineering, analytics, or equivalent quantitative field
• Experience leading teams to build production data pipelines, ELT/ETL solutions, data lakes, and data warehouses at a large scale
• 3+ years in a technology leadership role in data engineering building product ready event streaming solutions using Apache Kafka or AWS Kinesis
• 2+ years in a technology leadership role utilizing event-based monitoring and time series platforms (such as TimeScaleDB, VictoriaMetrics, InfluxDB, AWS Timestream)

• 2+ years in a technology leadership role implementing services (APIs/Endpoints) to enable data-driven product development

• 8+ years in data engineering on cloud data platform (min 5 years AWS)
• Programming proficiency in cloud data ecosystem using Python, R or Scala
• Microservice architecture experience using Golang based services
• Demonstrated leadership working with data engineers to build data pipelines using big data tooling (Spark, Hudi, Kafka, Kinesis)
• Demonstrated leadership and proficiency working in AWS cloud environment (S3, Cloud Formation, RDS, AWS Glue, Athena, EMR, Kinesis, Redshift)
• Exposure and experience with multiple data architecture stacks (data pipelines, data lakes, data warehouses, analytics stores, time series solutions)
• Strong understanding of cloud computing and optimization techniques
• Highly proficient with complex SQL

Preferred Qualifications

• AWS Certified Data Analytics - Specialty Certification
• Demonstrated leadership and specialized expertise in:
• Batch data ingestion and processing
• Analytical data modeling
• Data pipeline metadata observability
• Data platform cost optimization
• DataOps
• Specialized expertise designing and working with at least three (3) of the following data domains:
• Retail product data
• Ecommerce data
• Consumer profile data
• Clickstream data
• Consumer event data
• Marketing channel/campaign data
• Affiliate data
• Retailer/Brand data

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