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We’re scaling fast and need a hands-on Data Engineering Manager to join our dynamic Data Engineering team who can both lead people and shape data architecture. The ideal candidate possesses 3+ years of managing data engineers and 5+ years of experience working with PySpark, Python is a must. Data Bricks/ Snow Apache Iceberg/ Apache Flink/ and various orchestration tools, ETL pipelines, and data modeling. As our Data Engineering Manager, you will own the data-orchestration strategy end-to-end. You’ll lead and mentor a team of engineers while researching, planning, and institutionalizing best practices that boost our pipeline performance, reliability, and cost-efficiency. This is a hands-on leadership role for someone who thrives on deep technical challenges, enjoys rolling up their sleeves to debug or design, and can chart a clear, forward-looking roadmap for various data engineering projects.
Job Responsibility:
Lead, mentor, and grow a team of data engineers working on large-scale distributed data systems
Architect and oversee the development of end-to-end data solutions using AWS Data Services and Databricks
Hire, onboard, and develop a high-performing team—1-on-1s, growth plans, and performance reviews
Collaborate with cross-functional teams including data science, analytics, product, and business stakeholders to understand requirements and deliver impactful data products
Drive best practices in data engineering, coding standards, version control, CI/CD, and monitoring
Ensure high data quality, governance, and compliance with internal and external policies
Optimize performance and cost efficiency of data infrastructure in the cloud
Architect and evolve our data platform (batch & streaming) for scale, cost, and reliability
Own the end-to-end vision and strategic roadmap for various projects
Create documentation, architecture diagrams, and other training materials
Translate product and analytics needs into a clear data engineering roadmap and OKRs
Stay current with industry trends, emerging technologies, and apply them to improve system architecture and team capabilities
Requirements:
Bachelor’s or Master’s degree in Computer Science, STEM, or a related technical discipline
8+ years in data engineering (or adjacent), including 2-3+ years formally managing 1-3 engineers
Proven hands-on experience with: Big Data ecosystems (Spark, Hive, Hadoop)
Databricks (including Delta Lake, MLFlow, Unity Catalog)
Robust programming experience in Python and PySpark
Deep understanding of data modeling, ETL/ELT processes using Streaming, and performance tuning
Experience managing Agile teams and delivering complex projects on time
Excellent problem-solving, leadership, and communication skills
Experience designing and implementing Agentic Models with Data Pipelines (Data Cleaning and creative Feature Engineering)
Practical LLM/RAG experience for search quality such as query understanding, semantic retrieval, reranker design
You are a self-starter who enjoys working with both internal and external stakeholders
Nice to have:
Familiarity with ML/AI workflows and collaboration with data science teams