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Data Engineer

2 months ago

Hyderabad, India TOTLRPO Full time

Job Description :


- 14+ years of overall technology experience that includes at least 8+ years of hands-on software development, data engineering.

- 8+ years of experience in SQL optimization and performance tuning, and development experience in programming languages like Python, PySpark, Scala, etc.

- 2+ years in cloud data engineering experience in Azure Certification is a plus.

- Experience with data modeling, data warehousing, and building high-volume ETL/ELT pipelines.

- Experience with data profiling and data quality tools is a plus.

- The candidate must have thorough knowledge in Spark, SQL, Python, Databricks and Azure Spark (Joins, Upserts, Deletes, Aggregates, Repartitioning, Optimizations, working with structured and unstructured data, framework designing etc.)

- SQL (Joins, merge, aggregates, indexing, clustering, functions, stored procedures, optimizations etc.)

- Python (Functions, modules, classes, tuples, lists, dictionaries, lists, error handling, multi-threading etc.)

- Azure (Azure Data Factory, Service Bus, Log Analytics, Event Grid, Event Hub, Logic App, App services etc.) Databricks (Clusters, pools, workflows, authorization, APIs, DBRs, AQE, optimizations etc.)

Responsibilities :

- Active contributor to code development in projects and services.

- Manage and scale data pipelines from internal and external data sources to support new product launches and drive data quality across data products.

- Build and own the automation and monitoring frameworks that capture metrics and operational KPIs for data pipeline quality and performance.

- Responsible for adopting best practices around systems integration, security, performance, and data management defined within the organization.

- Collaborate with internal clients (data science and product teams) to drive solutions and POC discussions.

- Develop and optimize procedures to "productionalize" data engineering pipelines.

- Define and manage SLA's for data products and processes running in production.

(ref:hirist.tech)

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