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.