Experienced Lead Data Engineer with a strong track record in designing and implementing scalable data architectures, pipelines, and solutions. Skilled in AWS, Azure, GCP, Spark, Hadoop, and SQL/NoSQL databases to support…
Experienced Lead Data Engineer with a strong track record in designing and implementing scalable data architectures, pipelines, and solutions. Skilled in AWS, Azure, GCP, Spark, Hadoop, and SQL/NoSQL databases to support high-volume data processing and analytics. Expertise in ETL/ELT workflows, data modeling, and optimizing data storage for performance and reliability. Proven leader in guiding cross-functional teams to deliver high-quality, secure, and data-driven solutions. Committed to ensuring data governance, quality, and compliance while collaborating closely with data scientists and business stakeholders to drive insights and innovation.
Expertise in designing scalable data pipelines (ETL/ELT) using Apache Spark, Airflow, and Databricks. Deep knowledge of cloud platforms: AWS (Redshift, S3, Glue), Azure (Synapse, Data Factory), GCP (BigQuery). Architected and deployed modern data lakehouse solutions to support real-time and batch analytics. Strong background in SQL, Python, Scala, and data modeling (dimensional, star/snowflake schema). Led cross-functional teams to deliver high-performing, cost-efficient data platforms. Experience in implementing data governance, security, and compliance (GDPR, HIPAA). Collaborated with data scientists and business teams to enable machine learning and BI integration. Skilled in performance optimization, data quality monitoring, and metadata management. Proven track record of aligning data architecture with strategic business goals.
Proficient in developing ETL pipelines for batch and real-time processing of massive datasets. Skilled in data ingestion, transformation, and storage using HDFS, Parquet, ORC, and Avro formats. Deep understanding of big data frameworks and ecosystem tools (Airflow, Sqoop, Flink, NiFi). Experience with cloud-native big data platforms: AWS EMR, GCP Dataflow, and Azure HDInsight. Strong programming skills in Python, Scala, Java, and SQL for big data manipulation. Implemented data lakes and lakehouse architectures for high-volume analytics workloads.
Built real-time data pipelines using Apache Kafka, enabling low-latency analytics for 1M+ daily events. Designed and maintained PostgreSQL databases, optimizing schemas for high-throughput data ingestion. Developed Python scripts for data transformation, reducing processing errors. Collaborated with cross-functional teams to integrate Kafka with downstream systems like Elasticsearch. Contributed to CI/CD pipeline setup using Jenkins, streamlining deployment processes.
Expert in building scalable ETL/ELT pipelines using tools like Apache Spark, SQL, and Python. Proficient with BI tools including Tableau, Power BI, and Looker for creating actionable dashboards. Strong knowledge of data warehousing (Snowflake, Redshift, BigQuery) and data modeling techniques. Experience integrating structured and unstructured data from multiple sources (APIs, databases, flat files). Implemented data quality checks, monitoring systems, and automated reporting frameworks. Worked closely with stakeholders and executives to drive data-driven decision making. Hands-on with cloud ecosystems (AWS, Azure, GCP) for modern data architecture. Proven ability to optimize SQL queries, performance tune BI reports, and ensure data accuracy. Skilled in translating complex business requirements into efficient data solutions and visualizations.
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