About the role
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.
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