Data Engineering (US Working Hours)

SUMMARY:
The core advanced data engineering skillsetis a comprehensive combination of technical expertise, platform knowledge, and problem-solving abilities required to build, maintain, and optimize robust, scalable, and efficient data systems.

1. Data Architecture and Design

Data Modeling:
Create normalized and denormalized schemas (3NF, star, snowflake).
Design data lakes, warehouses, and marts optimized for analytical or transactional workloads.
Incorporate modern paradigms like data mesh, lakehouse, and delta architecture.
ETL/ELT Pipelines:
Develop end-to-end pipelines for extracting, transforming, and loading data.
Optimize pipelines for real-time and batch processing.
Metadata Management:
Implement data lineage, cataloging, and tagging for better discoverability and governance.

1. Distributed Computing and Big Data Technologies

Proficiency with big data platforms:
Apache Spark (PySpark, Sparklyr).
Hadoop ecosystem (HDFS, Hive, MapReduce).
Apache Iceberg or Delta Lake for versioned data lake storage.
Manage large-scale, distributed datasets efficiently.
Utilize query engineslike Presto, Trino, or Dremio for federated data access.

1. Data Storage Systems

Expertise in working with different types of storage systems:
Relational Databases (RDBMS): SQL Server, PostgreSQL, MySQL, etc.
NoSQL Databases: MongoDB, Cassandra, DynamoDB.
Cloud Data Warehouses: Snowflake, Google BigQuery, Azure Synapse, AWS Redshift.
Object Storage: Amazon S3, Azure Blob Storage, Google Cloud Storage.
Optimize storage strategies for cost and performance:
Partitioning, bucketing, indexing, and compaction.

1. Programming and Scripting

Advanced knowledge of programming languages:
Python (pandas, PySpark, SQLAlchemy).
SQL (window functions, CTEs, query optimization).
R (data wrangling, Sparklyr for data processing).
Java or Scala (for Spark and Hadoop customizations).
Proficiency in scripting for automation (e.g., Bash, PowerShell).

1. Real-Time and Streaming Data

Expertise in real-time data processing:
Apache Kafka, Kinesis, Azure Event Hub for event streaming.
Apache Flink or Spark Streaming for real-time ETL.
Implement event-driven architecturesusing message queues.
Handle time-series dataand process live feeds for real-time analytics.

1. Cloud Platforms and Services

Experience with cloud environments:
AWS: Lambda, Glue, EMR, Redshift, S3, Athena.
Azure: Data Factory, Synapse, Data Lake, Databricks.
GCP: BigQuery, Dataflow, Dataproc.
Manage infrastructure-as-code (IaC) using tools like Terraform or CloudFormation.
Leverage cloud-native features like auto-scaling, serverless compute, and managed services.

1. DevOps and Automation

Implement CI/CD pipelinesfor data workflows:
Tools: Jenkins, GitHub Actions, GitLab CI, Azure DevOps.
Monitor and automate tasks using orchestration tools:
Apache Airflow, Prefect, Dagster.
Managed services like AWS Step Functions or Azure Data Factory.
Automate resource provisioning using tools like Kubernetes or Docker.

1. Data Governance, Security, and Compliance

Data Governance:
Implement role-based access control (RBAC)and attribute-based access control (ABAC).
Maintain master data and metadata consistency.
Security:
Apply encryption at rest and in transit.

Apply encryption at rest and in transit.
Secure data pipelines with IAM roles, OAuth, or API keys.
Implement network security (e.g., firewalls, VPCs).
Compliance:
Ensure adherence to regulations like GDPR, CCPA, HIPAA, or SOC
Track and document audit trails for data usage.

1. Performance Optimization
   -

Optimize query and pipeline performance:
Query tuning (partition pruning, caching, broadcast joins).
Reduce IO costs and bottlenecks with columnar formats like Parquet or ORC.
Use distributed computing patterns to parallelize workloads.
Implement incremental data processing to avoid full dataset reprocessing.

1. Advanced Data Integration

Work with API-driven data integration:
Consume and build REST/GraphQL APIs.
Implement integrations with SaaS platforms (e.g., Salesforce, Twilio, Google Ads).
Integrate disparate systems using ETL/ELT toolslike:
Informatica, Talend, dbt (data build tool), or Azure Data Factory.

1. Data Analytics and Machine Learning Integration

Enable data science workflows by preparing data for ML:
Feature engineering, data cleaning, and transformations.
Integrate machine learning pipelines:
Use Spark MLlib, TensorFlow, or scikit-learn in ETL pipelines.
Automate scoring and prediction serving using ML models.

1. Monitoring and Observability

Set up monitoring for data pipelines:
Tools: Prometheus, Grafana, or ELK stack.
Create alerts for SLA breaches or job failures.
Track pipeline and job health with detailed logs and metrics.

1. Business and Communication Skills

Translate complex technical concepts into business terms.
Collaborate with stakeholders to define data requirements and SLAs.
Design data systems that align with business goals and use cases.

1. Continuous Learning and Adaptability

Stay updated with the latest trends and tools in data engineering:
E.g., Data mesh architecture, Fabric, and AI-integrated data workflows.

•  

Actively engage in learning through online courses, certifications, and community contributions:
Certifications like Databricks Certified Data Engineer, AWS Data Analytics Specialty, or Google Professional Data Engineer.

POSITION INFO:
The core advanced data engineering skillsetis a comprehensive combination of technical expertise, platform knowledge, and problem-solving abilities required to build, maintain, and optimize robust, scalable, and efficient data systems.

1. Data Architecture and Design

Data Modeling:
Create normalized and denormalized schemas (3NF, star, snowflake).
Design data lakes, warehouses, and marts optimized for analytical or transactional workloads.
Incorporate modern paradigms like data mesh, lakehouse, and delta architecture.
ETL/ELT Pipelines:
Develop end-to-end pipelines for extracting, transforming, and loading data.
Optimize pipelines for real-time and batch processing.
Metadata Management:
Implement data lineage, cataloging, and tagging for better discoverability and governance.

1. Distributed Computing and Big Data Technologies

Proficiency with big data platforms:
Apache Spark (PySpark, Sparklyr).
Hadoop ecosystem (HDFS, Hive, MapReduce).
Apache Iceberg or Delta Lake for versioned data lake storage.
Manage large-scale, distributed datasets efficiently.
Utilize query engineslike Presto, Trino, or Dremio for federated data access.

1. Data Storage Systems

Expertise in working with different types of storage systems:
Relational Databases (RDBMS): SQL Server, PostgreSQL, MySQL, etc.
NoSQL Databases: MongoDB, Cassandra, DynamoDB.
Cloud Data Warehouses: Snowflake, Google BigQuery, Azure Synapse, AWS Redshift.
Object Storage: Amazon S3, Azure Blob Storage, Google Cloud Storage.
Optimize storage strategies for cost and performance:
Partitioning, bucketing, indexing, and compaction.

1. Programming and Scripting

Advanced knowledge of programming languages:
Python (pandas, PySpark, SQLAlchemy).
SQL (window functions, CTEs, query optimization).
R (data wrangling, Sparklyr for data processing).
Java or Scala (for Spark and Hadoop customizations).
Proficiency in scripting for automation (e.g., Bash, PowerShell).

1. Real-Time and Streaming Data

Expertise in real-time data processing:
Apache Kafka, Kinesis, Azure Event Hub for event streaming.
Apache Flink or Spark Streaming for real-time ETL.
Implement event-driven architecturesusing message queues.
Handle time-series dataand process live feeds for real-time analytics.

1. Cloud Platforms and Services

Experience with cloud environments:
AWS: Lambda, Glue, EMR, Redshift, S3, Athena.
Azure: Data Factory, Synapse, Data Lake, Databricks.
GCP: BigQuery, Dataflow, Dataproc.
Manage infrastructure-as-code (IaC) using tools like Terraform or CloudFormation.
Leverage cloud-native features like auto-scaling, serverless compute, and managed services.

1. DevOps and Automation

Implement CI/CD pipelinesfor data workflows:
Tools: Jenkins, GitHub Actions, GitLab CI, Azure DevOps.
Monitor and automate tasks using orchestration tools:
Apache Airflow, Prefect, Dagster.
Managed services like AWS Step Functions or Azure Data Factory.
Automate resource provisioning using tools like Kubernetes or Docker.

1. Data Governance, Security, and Compliance

Data Governance:
Implement role-based access control (RBAC)and attribute-based access control (ABAC).
Maintain master data and metadata consistency.
Security:
Apply encryption at rest and in transit.

Apply encryption at rest and in transit.
Secure data pipelines with IAM roles, OAuth, or API keys.
Implement network security (e.g., firewalls, VPCs).
Compliance:
Ensure adherence to regulations like GDPR, CCPA, HIPAA, or SOC
Track and document audit trails for data usage.

1. Performance Optimization
   -

Optimize query and pipeline performance:
Query tuning (partition pruning, caching, broadcast joins).
Reduce IO costs and bottlenecks with columnar formats like Parquet or ORC.
Use distributed computing patterns to parallelize workloads.
Implement incremental data processing to avoid full dataset reprocessing.

1. Advanced Data Integration

Work with API-driven data integration:
Consume and build REST/GraphQL APIs.
Implement integrations with SaaS platforms (e.g., Salesforce, Twilio, Google Ads).
Integrate disparate systems using ETL/ELT toolslike:
Informatica, Talend, dbt (data build tool), or Azure Data Factory.

1. Data Analytics and Machine Learning Integration

Enable data science workflows by preparing data for ML:
Feature engineering, data cleaning, and transformations.
Integrate machine learning pipelines:
Use Spark MLlib, TensorFlow, or scikit-learn in ETL pipelines.
Automate scoring and prediction serving using ML models.

1. Monitoring and Observability

Set up monitoring for data pipelines:
Tools: Prometheus, Grafana, or ELK stack.
Create alerts for SLA breaches or job failures.
Track pipeline and job health with detailed logs and metrics.

1. Business and Communication Skills

Translate complex technical concepts into business terms.
Collaborate with stakeholders to define data requirements and SLAs.
Design data systems that align with business goals and use cases.

1. Continuous Learning and Adaptability

Stay updated with the latest trends and tools in data engineering:
E.g., Data mesh architecture, Fabric, and AI-integrated data workflows.

Actively engage in learning through online courses, certifications, and community contributions:
Certifications like Databricks Certified Data Engineer, AWS Data Analytics Specialty, or Google Professional Data Engineer.

 

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