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Job Summary
Data Architecture and Design- Data Modeling:
o Create normalized and denormalized schemas (3NF, star, snowflake).
o Design data lakes, warehouses, and marts optimized for analytical or transactional workloads.
o Incorporate modern paradigms like data mesh, lakehouse, and delta architecture.
- ETL/ELT Pipelines:
o Develop end-to-end pipelines for extracting, transforming, and loading data.
o Optimize pipelines for real-time and batch processing.
- Metadata Management:
o Implement data lineage, cataloging, and tagging for better discoverability and governance.
Distributed Computing and Big Data Technologies
- Proficiency with big data platforms:
o Apache Spark (PySpark, Sparklyr).
o Hadoop ecosystem (HDFS, Hive, MapReduce).
o Apache Iceberg or Delta Lake for versioned data lake storage.
- Manage large-scale, distributed datasets efficiently.
- Utilize query engines like Presto, Trino, or Dremio for federated data access.
Data Storage Systems
- Expertise in working with different types of storage systems:
o Relational Databases (RDBMS): SQL Server, PostgreSQL, MySQL, etc.
o NoSQL Databases: MongoDB, Cassandra, DynamoDB.
o Cloud Data Warehouses: Snowflake, Google BigQuery, Azure Synapse, AWS Redshift.
o Object Storage: Amazon S3, Azure Blob Storage, Google Cloud Storage.
- Optimize storage strategies for cost and performance:
o Partitioning, bucketing, indexing, and compaction.
Programming and Scripting
- Advanced knowledge of programming languages:
o Python (pandas, PySpark, SQL Alchemy).
o SQL (window functions, CTEs, query optimization).
o R (data wrangling, Sparklyr for data processing).
o Java or Scala (for Spark and Hadoop customizations).
- Proficiency in scripting for automation (e.g., Bash, PowerShell).
Real-Time and Streaming Data
- Expertise in real-time data processing:
o Apache Kafka, Kinesis, Azure Event Hub for event streaming.
o Apache Flink or Spark Streaming for real-time ETL.
o Implement event-driven architectures using message queues.
- Handle time-series data and process live feeds for real-time analytics.
Cloud Platforms and Services
- Experience with cloud environments:
o AWS: Lambda, Glue, EMR, Redshift, S3, Athena.
o Azure: Data Factory, Synapse, Data Lake, Databricks.
o 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.
DevOps and Automation
- Implement CI/CD pipelines for data workflows:
o Tools: Jenkins, GitHub Actions, GitLab CI, Azure DevOps.
- Monitor and automate tasks using orchestration tools:
o Apache Airflow, Prefect, Dagster.
o Managed services like AWS Step Functions or Azure Data Factory.
- Automate resource provisioning using tools like Kubernetes or Docker.
Data Governance, Security, and Compliance
- Data Governance:
o Implement role-based access control (RBAC) and attribute-based access control (ABAC).
o Maintain master data and metadata consistency.
- Security:
o Apply encryption at rest and in transit.
o Secure data pipelines with IAM roles, OAuth, or API keys.
o Implement network security (e.g., firewalls, VPCs).
- Compliance:
o Ensure adherence to regulations like GDPR, CCPA, HIPAA, or SOC 2.
o Track and document audit trails for data usage.
Performance Optimization
- Optimize query and pipeline performance:
o Query tuning (partition pruning, caching, broadcast joins).
o Reduce IO costs and bottlenecks with columnar formats like Parquet or ORC.
o Use distributed computing patterns to parallelize workloads.
- Implement incremental data processing to avoid full dataset reprocessing.
Advanced Data Integration
- Work with API-driven data integration:
o Consume and build REST/GraphQL APIs.
o Implement integrations with SaaS platforms (e.g., Salesforce, Twilio, Google Ads).
- Integrate disparate systems using ETL/ELT tools like:
o Informatica, Talend, dbt (data build tool), or Azure Data Factory.
Data Analytics and Machine Learning Integration
- Enable data science workflows by preparing data for ML:
o Feature engineering, data cleaning, and transformations.
- Integrate machine learning pipelines:
o Use Spark MLlib, TensorFlow, or scikit-learn in ETL pipelines.
- Automate scoring and prediction serving using ML models.
Monitoring and Observability
- Set up monitoring for data pipelines:
o Tools: Prometheus, Grafana, or ELK stack.
o Create alerts for SLA breaches or job failures.
- Track pipeline and job health with detailed logs and metrics.
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.
Continuous Learning and Adaptability
- Stay updated with the latest trends and tools in data engineering:
o E.g., Data mesh architecture, Fabric, and AI-integrated data workflows.
- Actively engage in learning through online courses, certifications, and community contributions:
o Certifications like Databricks Certified Data Engineer, AWS Data Analytics Specialty, or Google Professional Data Engineer.
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