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Data Architect
RARR Technologies
posted 18d ago
Job Role Insights
Flexible timing
Key skills for the job
Job Description
- Expertise in designing, implementing, and maintaining data solutions including delta lake, data warehouse, data marts and data pipelines on the Databricks platform that support business and technology objectives.
- Apply best practices during design in data modeling (logical, physical) and ETL pipelines (streaming and batch) using AWS cloud-based services.
- Proficiency in ETL implementation using AWS databricks, including hands on experience in predictive optimization, unity catalogue and Managed Delta tables.
- Design, develop and manage the pipelining (collection, storage, access), data engineering (data quality, ETL, Data Modelling) and understanding (documentation, exploration) of the data.
- Perform data transformation tasks, including data cleansing, aggregation, enrichment, and normalization, using Databricks and related technologies.
- Experience in extracting data from heterogenous sources vis. Flat Files, APIs, XML, RDBMs and implementing complex transformations vis. SCDs etc in databricks notebooks.
- Monitor and troubleshoot data pipelines, identifying and resolving performance issues, data quality problems, and other technical challenges.
- Implement best practices for data governance, data security, and data privacy within the Databricks environment.
- Interact with stakeholders regarding data landscape understanding, conducting discovery exercises, developing proof of concepts, and demonstrating it to stakeholders.
- Proven skill sets in AWS Data Engineering and Data Lake services such as - AWS Glue, S3, Lambda, SNS, IAM etc.
- Strong SQL, Python, PySpark scripting hands on knowledge/experience.
- Experience in data migration projects from On Prem to AWS Cloud.
- Experiences with design, develop, and implement end-to-end data engineering solutions using Databricks for large-scale data processing and data integration projects.
- Build and optimize data ingestion processes from various sources, ensuring data quality, reliability, and scalability.
- Ability to understand and articulate requirements to technical and non-technical audiences
- Experience in code conversion from native ETL to py-spark code.
- Collaborate with DevOps and infrastructure teams to optimize the performance and scalability of Databricks clusters and resources.
- Perform the code deployment using CICD
- Stakeholder management and communication skills, including prioritizing, problem solving and interpersonal relationship building
- Provide guidance and mentorship to junior data engineers, fostering a culture of knowledge sharing and continuous learning within the team.
- Etl, Sql, Data Warehouse, Aws, Ci/Cd, Data Bricks, Pyspark
Employment Type: Full Time, Permanent
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based on 12 employees
Flexible timing
Monday to Friday
No travel
Day Shift
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