| Databricks, Spark |
| Snowflake Cloud Data Platform |
| Data Vault 2.0 model |
| Enterprise Data Integrations |
| AWS Cloud architecture |
| Event and messaging patterns, streaming data, AWS, Kafka |
Role & Responsibilities:
Data Engineers will be responsible for design, build and maintain data pipelines ensuring data quality, efficient processing, and timely delivery of accurate and trusted data.
The ability to design, implement and optimize large-scale data and analytics solutions on Databricks, Spark, Snowflake Cloud Data Warehouse is essential.
Ensure performance, security, and availability of the data warehouse.
Establish ongoing end-to-end monitoring for the data pipelines.
Strong understanding of full CI/CD lifecycle.
Must Haves:
- 2+ years of recent experience with Databricks / Spark / Snowflake and a total of 6+ years in data engineering role.
- Designing and implementing highly performant data ingestion pipelines from multiple sources using spark and databricks.
- Extensive working knowledge of Spark and Databricks
- Demonstrable experience designing and implementing modern data warehouse/data lake solutions with an understanding of best practices.
- Hands-on development experience with Snowflake data platform including Snowpipes, SnowSQL,tasks, stored procedures, streams, resource monitors, RBAC controls, virtual warehouse sizing, query performance tuning, cloning, time travel, data sharing and understanding how to use these features.
- Advanced proficiency in writing complex SQL statements and manipulating large structured and semi-structured datasets.
- Data loading/Unloading and Data sharing
- Strog hands-on Experience on SNOWSQL queries, script preparation and stored procedures and performance tunning
- Knowledge of SnowPipe implementation
- Create Spark jobs for data transformation and aggregation
- Produce unit tests for Spark transformations and helper methods
- Security design and implementation on Databricks
- Build processes supporting data transformation, data structures, metadata, dependency and workload management.
- A successful history of manipulating, processing and extracting value from large disconnected datasets.
- Working knowledge of message queuing, stream processing, and scalable 'big data' data stores.
Good to Have:
- Valid professional certification
- Experience in Python/Pyspark/Scala/Hive Programming.
- Excellent verbal and written communication and interpersonal skills
- Confidence and agility in challenging times
Ability to work collaboratively with cross-functional teams in a fast-paced, team environment