Key skills
Data EngineeringSQLPythonSparkPySparkScalaSnowflakeDatabricksBigQueryAmazon RedshiftData Modeling - KimballData Modeling - Data VaultKafkaFlinkTerraformCI/CD pipelinesAWSGCPAzureAIMLETLELTAnalyticsBusiness IntelligenceRedshiftAirflowdbtFivetranVaultCI/CDMentoring
About this role
Evnek is seeking a highly skilled and experienced Lead Data Engineer to build and scale modern data platforms that power analytics, reporting, and AI/ML initiatives. In this role, you will own the end-to-end data lifecycle, architect scalable data solutions, and lead a team of engineers to deliver reliable, high-performance data infrastructure.
Responsibilities:
- Design, build, and maintain scalable batch and real-time data pipelines
- Architect and implement modern Lakehouse solutions using technologies such as Delta Lake or Apache Iceberg
- Develop and manage ETL/ELT workflows using tools like Airflow, dbt, or Prefect
- Implement robust data quality, governance, lineage, and monitoring frameworks
- Design optimized data models to support analytics, business intelligence, and ML workloads
- Improve platform scalability, reliability, performance, and cost efficiency
- Collaborate with Analytics, Product, and AI/ML teams to enable data-driven solutions
- Lead and mentor a team of data engineers while driving engineering best practices and standards
- Build and maintain CI/CD pipelines and infrastructure automation for data platforms
- Ensure platform reliability, observability, and operational excellence across the data ecosystem
Requirements:
- 7–10 years of experience in Data Engineering, with at least 3+ years in a lead or mentoring role
- Strong expertise in SQL, Python, and Spark (PySpark/Scala)
- Hands-on experience with modern cloud data platforms such as: Snowflake, Databricks, BigQuery, Amazon Redshift
- Strong understanding of data modeling methodologies including Kimball and Data Vault
- Experience working with streaming and real-time data systems such as Kafka, Flink, or similar technologies
- Familiarity with infrastructure-as-code and DevOps practices using Terraform and CI/CD pipelines
- Hands-on experience with cloud platforms including AWS, GCP, or Azure
- Strong understanding of scalable distributed data systems and modern data architecture patterns
- Experience with feature stores such as Feast or Tecton
- Familiarity with data mesh concepts and CDC tools like Fivetran or Airbyte
- Exposure to graph databases and vector databases
- Experience contributing to open-source projects is a plus
- Advanced degree in Computer Science, Data Engineering, or a related field preferred