We are:
Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.
With the right people and the right ideas, there’s no limit to what we can achieve
Are you a fit?
Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:
Responsibilities:
Existing platform (Databricks)
- Keep production pipelines running: ingestion, transformation, and delivery to downstream consumers.
- Diagnose and resolve pipeline failures and data quality issues, often without documentation to fall back on.
- Reverse-engineer and document existing transformation logic and business rules — this is the input the migration depends on.
- Migrate legacy tables from Hive Metastore to Unity Catalog.
- Maintain Iceberg-enabled table sharing between Databricks and Snowflake.
New development (Snowflake, dbt, Airflow)
- Build and test dbt models, including incremental materializations and data tests.
- Develop and maintain Airflow DAGs for orchestration.
- Validate that migrated pipelines produce output equivalent to the Databricks versions.
- Contribute to Snowflake modeling, performance, and cost decisions.
Across both
- Work directly with client stakeholders on technical topics, alongside the team lead.
Technical Requirements
Databricks
- PySpark and SQL — able to read, debug, and modify existing pipelines. Deep Spark tuning is not required.
- Delta Lake: MERGE/upsert patterns, table properties, OPTIMIZE, partitioning.
- Databricks Workflows, cluster configuration, job troubleshooting.
- Unity Catalog: catalogs, schemas, grants, lineage, and the metastore model.
Snowflake
- Warehouses, roles and grants, and the general operating model.
- Query performance and an awareness of how compute cost behaves.
Dbt
- Models, sources, tests, and incremental materializations.
- Project structure and how dbt fits into a deployment workflow.
Airflow
- Writing and maintaining DAGs, operators, scheduling, and dependency management.
- Understanding retries, backfills, and idempotent task design.
Fundamentals
- 3+ years operating production data pipelines.
- Strong SQL — window functions, complex joins, reading transformation logic written by someone else.
- Python for scripting, automation, and API integration.
- Incremental loading patterns, idempotency, late-arriving data, reprocessing.
- AWS: S3, IAM basics. Basic working knowledge of Redshift and its role in the wider architecture.
Ways of working
- Fluent English — client-facing role with stakeholders based abroad.
- Self-directed. Able to make progress on an unfamiliar codebase without a structured onboarding path, and comfortable asking good questions when context is missing.
- Clear communicator: can explain a production incident to a non-technical stakeholder and give a realistic ETA.
Nice-to-have:
- Experience with an actual platform migration, not only greenfield work.
- Open table formats, particularly Iceberg and cross-platform sharing.
- Clickstream or web analytics data (Adobe Analytics, Google Analytics, Segment).
- Experience taking over an undocumented system and stabilizing it.
- AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
What we offer:
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
- Specific benefits are determined by the employment type and location.
Find out more about our culture here.
