Senior Data Engineer

3 days ago

Islamabad, Islamabad, Pakistan DigyCorp Full time 900,000 - 1,200,000 per year

Job Title:
Senior Data Engineer - Azure & Databricks

Location:
Islamabad

Contract Period:
One Year (Extendable)

Experience:
5+ years of experience

Seniority level:
Mid/Senior

We Have:


• Mission-led project that will revolutionize our ability to restore coral reefs affected by global warming.


• Team of Developers, Architects, ML Engineers, Bank-End Engineers and Front-End Developers interacting with back-end service architecture deployed on a Cloud Platform.


• Innovation, team culture is at the heart of what we do.


• An international team that promotes a multicultural working environment.


• Offering great career growth opportunities working for international top clients within the global technology market.


• An open mind for new ideas and methodologies and in fact the person filling the role is encouraged to come up with new data business concepts/products/features etc.


• Adaptability. We share data knowledge and push to develop as innovation to create solutions that enable our clients goals.


• A passion for solving hard problems.

You Have:


• Develop scalable and efficient data pipelines using
Azure Data Factory, Databricks, and Synapse Analytics
.


• Experience with
Programming languages like Python and libraries like PySpark, Spark Streaming.


• Experience with working
data frames like Pandas and Spark Dataframes.


• Implement data storage solutions such as
Azure Data Lake, Azure SQL Database, and Cosmos DB.


• Implement data pipelines to feed real-time and batch data to
AI/ML models.


• Support deployment and monitoring of machine learning models using
Azure Machine Learning and MLOps best practices.


• Integrate data from various on-premises and cloud sources.


• Create and optimize
Extract, Transform, Load (ETL) processes to ensure data quality and consistency.


• Design and implement data models for both operational and analytical systems.


• Ensure alignment with business and technical requirements.


• Monitor and tune data pipelines and databases to optimize performance and reduce costs.


• Implement caching strategies and partitioning to enhance data retrieval times.


• Implement data security measures, including encryption and access controls, following best practices and organizational standards.


• Ensure compliance with relevant regulations such as
GDPR and HIPAA.


• Work closely with data analysts, data scientists, and business stakeholders to understand data needs and deliver actionable insights.


• Utilize third-party tools like
Tableau, Power BI, or Looker for data visualization and advanced analytics.


• Collaborate with DevOps teams to automate and manage data workflows.


• Set up monitoring for data pipelines to proactively identify and resolve issues.


• Perform root cause analysis for any data discrepancies or failures.


• Design and implement scalable data models tailored for AI/ML and analytical workloads.


• Support feature engineering and data preparation processes for machine learning models.

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