Azure Data Engineer
1 month ago
As a member of the Data Engineering team, you will:
Implement continuous improvements to our data governance practices and implement data
quality improvements.
Partner with key stakeholders to understand and refine data product requirements.
Design and develop reliable data product pipelines that transform data at scale, orchestrated
via Azure Data Factory, using SQL and Python in Azure Databricks.
What you will bring to Databerry Technologies:
Experience with relational and MPP databases such as SQL Server and Azure Synapse Analytics.
Experience with data modeling techniques (Kimball/Star, Datavault, etc).
Experience with SQL and query design on large, complex datasets.
Experience with cloud and big-data tools and frameworks like Azure Databricks and Azure Data
Factory.
Expertise in designing and developing with distributed data processing platforms.
Experience using Azure Data Factory to develop data pipelines.
Experience using ELT/ETL tools such as Azure Data Factory and Azure Databricks (with
Python/PySpark).
Familiarity with API ingestion processes.
CI/CD with Terraform (Nice to have).
Ability to learn new technical concepts quickly.
Ability to operate in a fast-paced and dynamic environment.
Strong collaboration and communication skills.
Passion
We are passionate about our mission and technology, and we want you to
demonstrate that too.
Ownership
We want you to own your work, be accountable, and push it through to the finish line.
Expertise
We do not need you to know everything, but we hope you have deep knowledge in
at least one area and can start contributing quickly. We would love to learn from you in your
area(s) of expertise.
Key Attributes Required:
People Skills:
Ability to work effectively in cross-functional teams and collaborate openly with data engineers,
software engineers, and other stakeholders.
Proven track record in conflict resolution, effective communication, and fostering a positive
work environment.
Customer Service / External Impact:
Strong focus on internal customer service to assist software and data engineering teams in
achieving their objectives.
The ability to effectively communicate with external vendors, client-facing teams, and
sometimes clients themselves to ensure seamless deployment and maintenance of services.
Decision-Making:
Capacity for high-level, strategic decision-making to choose appropriate tools and technologies,
while also being capable of low-level technical decisions required for immediate problemsolving.
Ability to weigh pros and cons and consult with teams for decisions that impact system uptime,
security, and the delivery process.
Initiative and Independence:
Self-starter who can independently identify issues and formulate solutions with minimal
supervision.
Comfortable taking the initiative to lead projects or research and introduce new technologies
that can benefit the system's efficiency, security, and performance.
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