Director Data Engineering
21 hours ago
Orlando
Director of Data Engineering Remote Opportunity Orlando, FL Must be 40% Hands on Technical (Can still code with the best of them) and 60% Leadership, Strategy, Vision Salary: $225k-$250k Base and Annual Bonus Healthcare Benefits, Dental, Vision, 6% Match on 401k Overview We are seeking a highly technical and execution-driven Director of Data Engineering who can lead both our Core Data Engineering team and our B2B marketing data platform. This role requires a hands-on AWS-native expert who can architect scalable data frameworks, build high-quality data products, and elevate engineering excellence across the organization. You will guide a team of 10–15 engineers while serving as the technical backbone for our AWS-based data ecosystem—spanning ingestion, orchestration, warehousing, analytics, and privacy-safe marketing activation. The ideal candidate blends strong technical depth with the leadership maturity to influence product, marketing, privacy, governance, and C-level stakeholders. You thrive in ambiguity, make proactive decisions, and build systems that stand the test of scale, reliability, compliance, and cost efficiency. Responsibilities Strategic & Product Leadership • Own the technical vision and long-term roadmap for Core Data Engineering and the B2B marketing data platform., • Drive measurable improvements in data reliability, availability, quality, and time-to-insight., • Partner with Product to co-own platform strategy, ensuring value delivery to internal users and external enterprise clients., • Anticipate architectural evolution needs, including:, • Medallion/lakehouse patterns, • Privacy-safe audience activation, • Clean room innovation, • Marketing data integrations, • Navigate cross-functional alignment with C-level leaders, providing clarity, influence, and strong narrative around technical decisions. Team & Execution Leadership • Lead, mentor, and scale a high-performing engineering team with a culture grounded in:, • Accountability, • Autonomy, • Excellence, • Continuous improvement, • Drive clarity around roles, expectations, and ownership; build mission-driven teams that deliver., • Establish engineering frameworks and best practices that minimize manual work and maximize reusability., • Partner with Data Governance, Legal, and Privacy teams to ensure compliant use of first-party data. Technical Leadership & Architecture (Critical hiring manager priority — must be AWS-native and hands-on) • Architect end-to-end AWS data systems, including:, • AWS Glue (PySpark/ETL), • Step Functions & MWAA (Airflow) for orchestration, • Redshift + Iceberg storage patterns, • Lambda, API Gateway, Cognito for serverless integrations, • Design metadata-driven, scalable frameworks—not just pipelines—including:, • Dynamic ingestion frameworks, • Reusable transformation patterns, • Schema evolution and lineage tracking, • Oversee integrations with marketing, adtech, social, and transactional platforms., • Guide deployment and operationalization of ML models (segmentation, recommendations, forecasting)., • Ensure robust IAM practices, cost optimization, observability, and disaster recovery readiness., • Create engineering “golden paths” and standardization across the DE org. Qualifications Required • 10+ years in data engineering roles, with 5–7+ years leading data or platform engineering teams., • Deep, hands-on expertise with AWS-native technologies:, • Glue, Step Functions, MWAA (Airflow), • Lambda, API Gateway, Cognito, • Redshift (including tuning), Iceberg tables, S3, • SNS/SQS, EventBridge, Batch, • Strong experience architecting big data, ETL, medallion/lakehouse, and distributed systems., • Proficiency in Python, PySpark, SQL, and API/microservices patterns., • Strong knowledge of IAM, cost optimization, monitoring/alerting, and CI/CD (GitHub Actions, Terraform/CloudFormation)., • Proven experience aligning engineering with business KPIs, marketing needs, and product goals., • Experience building engineering frameworks (not just pipelines)., • Experience supporting marketing platforms, clean rooms, or first-party data activation., • Experience with metadata-driven orchestration or pipeline engines., • ML model deployment experience (segmentation, LTV, recommendation pipelines)., • Strong SDLC, agile leadership, and architectural documentation skills. What Success Looks Like • A reliable, scalable, AWS-native data platform powering internal and external data products., • Material improvements in:, • Time-to-insight, • Data reliability, • Pipeline reusability, • Cost efficiency, • A high-performing, growing, and diverse engineering team., • Clear engineering frameworks that standardize how data moves from ingestion → transformation → analytics → activation., • Trusted partnerships with Product, Privacy, Data Science, and C-level stakeholders.