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Mid-level data engineer with Databricks specialization attracts moderate competition in consulting metro markets.
Databricks and big-data expertise moderately industry-transferable but favors data-platform roles.
Explicit 4+ years and mandatory 2+ years Databricks plus specific tech stack increases screening strictness.
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Own data engineering tasks on both internal and client projects, focusing on big data technologies and Databricks platform.
Develop and maintain data architectures, ETL pipelines, and data warehousing solutions leveraging cloud platforms like AWS.
Ensure quality code delivery and proactively address client needs with future-proof solutions.
Minimum 4 years of experience in data engineering with big data technologies such as Spark and Kafka.
At least 2 years of hands-on experience with Databricks required.
Proficiency in programming with Python or Java and strong knowledge of cloud platforms (AWS, Azure, or GCP).
Work Experience Required: 4+ years in relevant data engineering roles.
Experienced in delivering data engineering solutions in fast-paced, client-facing environments with complex data processing requirements.
Skilled in managing multiple priorities while maintaining high quality and professionalism.
Strong communicator with demonstrated ability to work independently and collaboratively in team settings.