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As part of the Data Engineering team, the Lead Python Data Engineer will work closely with Business domain experts and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics, machine learning, and artificial intelligence.
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Experience with distributed large-scale data processing (preferably Apache Spark). This is a full-time hybrid role for a Machine Learning Engineer at Mastech Digital. Knowledge of streaming technologies (Apache Kafka, Spark, Flink, and AWS Kinesis.
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Proficient in programming in Java or Python with prior Apache Beam/Spark experience a plus. Hands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required.
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Expertise in two or more data estate workloads like Teradata, Oracle Exadata, IBM Netezza, SAP (HANA, BW), HDInsight, Hadoop, Cloudera/Hortonworks, Apache Spark, Snowflake, MapR, AWS (Redshift, Glue), Google (BigQuery), MemSQL, MarkLogic, Qubole, Pivotal (Greenplum), Actian (Avalanche), Alibaba (MaxCompute) required.
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Advanced knowledge of the Hadoop ecosystem and Big Data technologies Hands-on experience with the Hadoop eco-system (HDFS, MapReduce, Hive, Pig, Impala, Spark, Kafka, Kudu, Solr) Partner with domain experts, product managers, analysts, and data scientists to develop Big Data pipelines in Hadoop or Snowflake.
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Experience with AWS services - Amazon MSK/Apache kafka, ECS, S3, IAM, AWS XRay. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action.
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Experience in Big Data platforms and related technologies such as Hadoop, Hive, Pig, Apache Spark, Teradata etc. Machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.
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Job Description: Joining CoreLogic means working on the cutting edge of the industry with next-gen technology such as cloud native applications, machine learning, artificial intelligence, augmented reality, drones, and more.
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Experience with AWS services - Amazon MSK/Apache kafka, ECS/kubernetes, S3, IAM, AWS XRay. Experience with AWS services - Amazon MSK/Apache kafka, ECS/kubernetes, S3, IAM, AWS XRay. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.
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Big Data-Hadoop, NoSQL, Hive, Apache Spark. Exposure to machine learning engineering. Big Data-Hadoop, NoSQL, Hive, Apache Spark. Exposure to NLP and text processing. Business Domain knowledge: Finance & banking systems, Fraud, Payments.
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Experience with data engineering tools and platforms (e.g., Apache Spark, Kafka, RedPanda, Hadoop, etc). We need a solid team player with enterprise level Architect experience and a background in advanced data analytics, machine learning models and artificial intelligence solutions.
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The ideal candidate will have in-depth knowledge of Apache Spark, Pyspark, and Databricks architecture, strong programming skills in Python or Scala, and experience with data engineering concepts, ETL processes, and data modeling.
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Expertise in Open source Data Technologies in processing, storage, data quality - Cloudera Hadoop, Presto HUDI, Iceberg, Delta Lake, Apache Flink, GreatExpectations, Airflow, Kubernetes, Kafka. Building, and producing petabyte-scale data pipelines, lakehouses, and machine learning infrastructure on Google Cloud Platform leveraging tools like BigQuery, Dataflow, Dataproc, and Vertex AI.
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In-depth knowledge of Apache Spark, Pyspark and Databricks architecture. Oversee the implementation of data processing workflows, analytics, and machine learning models using Databricks.
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Familiarity with advanced statistical and machine learning techniques such as clustering, gradient boosting machines (GBM), support vector machines (SVM), neural networks (e.g., ANN, RNN, CNN), and other deep learning algorithms is a plus.
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