Responsibilities
- Design Scalable Data Architecture: Build modern, cloud-native data platforms (AWS, Snowflake, Databricks) supporting batch and streaming use cases.
- Develop Efficient Data Pipelines & Models: Automate ETL/ELT workflows, optimise data models, and enable self-serve analytics and AI.
- End-to-End Data Ownership: Manage ingestion, storage, processing, and delivery of structured and unstructured data.
- Performance & Cost Optimisation: Continuously tune infrastructure for high concurrency, low latency, and cost efficiency.
- Real-Time Integration & Analytics: Ingest telemetry, API, and application data in real time to power dashboards and AI-driven tools.
- ML & AI Enablement: Provision datasets for ML/AI workloads, integrating with SageMaker, Snowflake ML, and MLOps best practices.
- Data Governance & Security: Ensure robust data governance, compliance (GDPR, SOC 2), and enterprise-grade security.
- Collaboration & Strategy: Work closely with Product, Engineering, DevOps, and Analytics teams to align data solutions with business goals.
Requirements
- significant experience in technology roles
- 5+ years in data engineering on real-time, scalable cloud platforms (AWS & Snowflake preferred)
- AWS (S3, Glue, Lambda, Athena, Kinesis)
- Snowflake (data pipelines, schema design, query optimization)
- Data modeling, ETL/ELT, real-time streaming (Kafka, Kinesis)
- Big data processing (Spark, Airflow), SQL, Python, Java/Scala
- BI & analytics platforms (Tableau, Looker)
- ML/AI integration (SageMaker, TensorFlow, Snowflake ML, feature stores)
- Data governance, security, and compliance frameworks
- Strong communicator, collaborative, analytical, and strategic
- Ability to balance multiple projects while driving innovation and operational excellence
Nice to Have
- Experience in SaaS/product companies managing large-scale IoT, telemetry, or digital datasets
Work Arrangement
Remote (Worldwide)
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