Technical Leadership & Solution Design

• Lead design for complex data subsystems (ingestion, transformation, curation, serving)  
• Drive key design choices such as batch vs streaming, lakehouse vs warehouse, orchestration approach, and partitioning strategy  
• Define and enforce standards for pipeline design, coding practices, documentation, and production readiness  

Hands-on Data Engineering Ownership

• Design, build, and optimize ETL / ELT pipelines using Python, SQL, and distributed processing frameworks (Spark / Databricks)  
• Own complex integrations across multiple source systems (APIs, files, CDC/event streams)  
• Optimize pipelines for performance, reliability, and cost efficiency

Orchestration, Reliability & Operations

• Architect and implement orchestration patterns (Airflow or equivalent), including retries, backfills, SLAs, and dependency management  

• Establish monitoring, alerting, and data quality checks  
• Lead triage and resolution of high-severity data pipeline issues  

Platform Enablement (Applied, Not Architectural-Heavy)

• Implement CI/CD practices for data pipelines  
• Build reusable components and templates (ingestion patterns, validation utilities, standard DAGs) to accelerate delivery  
• Partner with Architects to align solutions with modern cloud data platform patterns where needed

Mentoring & Collaboration

• Mentor Senior and mid-level engineers through design and code reviews  
• Clearly communicate design decisions and trade-offs to engineers, product teams, and clients/stakeholder  

Design & Architecture Articulation

• Produce and maintain design docs and Architecture Decision Records (ADRs) including trade-offs, risks, and rollout plans  
• Communicate design intent and responsibilities across teams clearly  

• Overall experience: 7+ years  
• Strong proficiency in Python and SQL  
• Deep hands-on experience with Spark / PySpark / Databricks  
• Solid experience designing production-grade ETL / ELT pipelines and orchestration workflows  
• Strong understanding of data modeling, data warehouses, and data lakes  
• Practical experience with cloud platforms (AWS / Azure / GCP)  
• Proven ability to lead technical decisions, mentor engineers, and deliver under ambiguity  

• Experience with streaming or event-driven data pipelines (Kafka, Kinesis, Pub/Sub)  
• Exposure to data observability, governance, or catalog tools  
• Familiarity with containers, IaC, or platform enablement practices  
• Relevant cloud or data engineering certifications