proven experience designing and implementing data platforms using Databricks
. In this mid-level role, you will play a critical part in architecting scalable data solutions that drive analytics, data science, and business intelligence efforts. You will work cross-functionally with engineering, analytics, and infrastructure teams to transform raw data into valuable enterprise assets.
Developing and maintaining robust data pipelines, including batch and streaming workloads, to support data ingestion, processing, and consumption.
Leading technical efforts in data quality, metadata management, data cataloguing, and governance (including Unity Catalogue if applicable).
Providing technical guidance to junior engineers and analysts in the adoption of modern data architecture patterns.
Evaluating and recommending emerging tools and frameworks within the Databricks ecosystem and broader data engineering space.
Having a solid understanding of analytics engines and columnar databases to support performance-optimised data solutions.
Experience with full-text search platforms is highly desirable; familiarity with technologies like Elasticsearch or Solr is a strong advantage.Designing and implementing cloud-native data architectures using Databricks and technologies such as Delta Lake, Spark, and MLflow.Collaborating with business stakeholders and analytics teams to define data requirements, data models, and data integration strategies.Ensuring data architecture solutions are secure, scalable, and high performing, adhering to enterprise standards and best practices.Hands-on experience in
data architecture
data engineering
, or a similar role.Proficiency in
data modelling
SQL
data warehousing
, and
ETL frameworks
.Hands-on experience with
CI / CD pipelines
, version control (Git), and DevOps practices.Familiarity with