Sleek is looking for a skilled Data Engineer to join their growing Data Platform team. The ideal candidate will design, build, and maintain robust data pipelines on Databricks and AWS infrastructure, and have experience with ETL/ELT design patterns, ingestion patterns, and database internals.
Requirements
- 3+ years of professional experience in data engineering
- Strong understanding of data platform architecture: Lakehouse, Data Warehouse, Data Lake patterns
- Hands-on experience with ETL/ELT design patterns including batch processing and stream processing
- Familiarity with ingestion patterns: full load, incremental, CDC, event-driven
- Strong understanding of database internals — storage engines, transactions, isolation levels, locking, MVCC, query planners
- Proven experience supporting mission-critical OLTP workloads with high availability requirements
- Solid scripting skills in Bash and/or Python for automation
- Experience building data pipelines on Databricks (Delta Live Tables, Jobs, Notebooks)
- Proficiency with PySpark or Spark SQL for large-scale data processing
- Familiarity with Delta Lake concepts: ACID transactions, time travel, schema evolution
- Proficiency with Apache Airflow — authoring, scheduling, and monitoring DAGs
- Experience with Airbyte for managing source-to-destination data connectors
- Hands-on experience administering MongoDB (self-managed and/or Atlas)
- SQL & dbt:
- Strong SQL skills — query optimization, window functions, CTEs, and complex joins
- Experience with dbt (data build tool) for transformation, testing, and documentation
- Model layering: staging → intermediate → marts
- Writing schema tests, source freshness checks, and macros
- Cloud & Infrastructure
- Practical experience with AWS services (S3, Lambda, IAM, CloudWatch, etc)
- Nice to have:
- Experience with Docker & Kubernetes (EKS) for deploying and scaling data services
- Experience running Airflow & Airbyte on Kubernetes
- Experience with data quality frameworks (Great Expectations, Soda
- Infrastructure as Code experience (Terraform)
- Exposure to data governance tools or data cataloging (Databricks Catalog)
- Familiarity with CI/CD pipelines for data engineering (GitHub Actions)
- Experience with Python for pipeline scripting and automation
Benefits
- Generous paid time off
- Holiday schedules
- Flexi benefits scheme for home office equipment or health and fitness expenditure
- Employee share ownership plan
- Autonomy and responsibility
- Range of internal and external facing training programmes
- Opportunities for personal growth
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