Lead Data Engineer
3 weeks ago
- Overall 5-7 years ofexperience in data engineering and transformation onCloud
- 3+ Years of Very Strong Experience inAzure Data Engineering, Databricks
- Expertise insupporting/developing lakehouse workloads at enterpriselevel
- Experience in pyspark is required– developing and deploying the workloads to run on theSpark distributed computing
- Candidate mustpossess at least a Graduate or bachelor's degree inComputer Science/Information Technology, Engineering(Computer/Telecommunication) orequivalent.
- Cloud deployment: PreferablyMicrosoft azure
- Experience in implementing theplatform and application monitoring using Cloud nativetools
1. DataArchitecture &Design
- Strongunderstanding of database architectures, including relational,NoSQL, and data warehousing.
- Ability to designand implement data pipelines, data lakes, and datawarehouses.
- Knowledge of ETL (Extract,Transform, Load) processes.
2.Programming &Scripting
- Languages:Expertise in Python, Java, Scala, andSQL.
- Familiarity with datamanipulation libraries like Pandas, Dask, and NumPy(for Python).
- Ability to write efficientqueries and scripts for data processing andanalysis.
3. Big DataTechnologies
- Proficiencyin big data frameworks like Hadoop,Spark, andFlink.
- Experiencewith distributed computing systems and handling largedatasets.
4. CloudPlatforms
- Knowledge ofcloud platforms such as AWS,Google Cloud Platform (GCP), andMicrosoftAzure.
- Experience withcloud-based data solutions like AmazonRedshift, GoogleBigQuery, or Azure DataLake.
5.DataModeling
- Expertise indata modeling techniques, such as dimensionalmodeling, ER modeling,and star/snowflakeschemas.
- Ability to designschema structures that support business intelligence and analyticalneeds.
6. Data PipelineManagement
- Experiencein creating and maintaining data pipelines for large-scale dataprocessing.
- Familiarity with tools likeApache Airflow,Luigi, ordbt for orchestratingworkflows.
7. DataWarehousing & SQLOptimization
- Expertisein designing and optimizing data warehousingsolutions.
- Strong skills in SQLtuning, optimization, and performance improvementfor handling large datasets.
8.Data Governance &Security
- Understandingof data governance practices, such as data privacy, metadatamanagement, and ensuring data quality.
- Abilityto enforce data security standards andpractices for data protection.
9.Team Leadership & ProjectManagement
- Ability tolead a team of data engineers, collaborate with cross-functionalteams, and manage data engineeringprojects.
- Familiarity with project managementframeworks like Agile andScrum.
10.Automation &CI/CD
- Experience inautomating data workflows and deploymentpipelines.
- Knowledge ofCI/CD tools (e.g., Jenkins, GitLab)for version control andautomation.
11. MachineLearning Integration(Optional)
- While notstrictly necessary for all data engineering roles, experienceintegrating data pipelines with machine learning models orsupporting the deployment of models can bevaluable.
1.Data Architecture &Design
- Strongunderstanding of database architectures, including relational,NoSQL, and data warehousing.
- Ability to designand implement data pipelines, data lakes, and datawarehouses.
- Knowledge of ETL (Extract,Transform, Load) processes.
2.Programming &Scripting
- Languages:Expertise in Python, Java, Scala, andSQL.
- Familiarity with datamanipulation libraries like Pandas, Dask, and NumPy(for Python).
- Ability to write efficientqueries and scripts for data processing andanalysis.
3. Big DataTechnologies
- Proficiencyin big data frameworks like Hadoop,Spark, andFlink.
- Experiencewith distributed computing systems and handling largedatasets.
4. CloudPlatforms
- Knowledge ofcloud platforms such as AWS,Google Cloud Platform (GCP), andMicrosoftAzure.
- Experience withcloud-based data solutions like AmazonRedshift, GoogleBigQuery, or Azure DataLake.
5.DataModeling
- Expertise indata modeling techniques, such as dimensionalmodeling, ER modeling,and star/snowflakeschemas.
- Ability to designschema structures that support business intelligence and analyticalneeds.
6. Data PipelineManagement
- Experiencein creating and maintaining data pipelines for large-scale dataprocessing.
- Familiarity with tools likeApache Airflow,Luigi, ordbt for orchestratingworkflows.
7. DataWarehousing & SQLOptimization
- Expertisein designing and optimizing data warehousingsolutions.
- Strong skills in SQLtuning, optimization, and performance improvementfor handling large datasets.
8.Data Governance &Security
- Understandingof data governance practices, such as data privacy, metadatamanagement, and ensuring data quality.
- Abilityto enforce data security standards andpractices for data protection.
9.Team Leadership & ProjectManagement
- Ability tolead a team of data engineers, collaborate with cross-functionalteams, and manage data engineeringprojects.
- Familiarity with project managementframeworks like Agile andScrum.
10.Automation &CI/CD
- Experience inautomating data workflows and deploymentpipelines.
- Knowledge ofCI/CD tools (e.g., Jenkins, GitLab)for version control andautomation.
11. MachineLearning Integration(Optional)
- While notstrictly necessary for all data engineering roles, experienceintegrating data pipelines with machine learning models orsupporting the deployment of models can bevaluable.
Engineering,EngineeringMathematics,Data Reporting
Employment Type: Part-time
Department / FunctionalArea: Data Engineering
Experience: Not Mentioned years
Gender: Male
Vacancy: 1
Joining Date: Wed, 19 Feb2025
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