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Interface with both relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases(e.g., MongoDB, Elasticsearch) to store, retrieve, and manipulate geospatial data efficiently. We are seeking a skilled and motivated Software Engineer to join our team in developing cutting-edge geospatial analysis, ETL (Extract, Transform, Load), and visualization microservices to supportmission-focused systems.
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Programming in Python or Ruby, utilizing AWS S3, MongoDB, PostgreSQL, AWS Redshift or similar database technologies; data engineering working on ETL pipelines, crawling APIs and websites, and automating outputs (report generation, workflow automation, Google Sheet interaction.
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Knowledge of big data platforms like Snowflake, DBT, AWS Redshift, Postgres, MongoDB, and Hadoop. Strong experience in building ETL data pipelines and analysis using Python, SQL, and PySpark.
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Erfahrungen in folgenden Bereichen sind von Vorteil: Snowflake, Databricks, Azure Synapse, SQL-Server, PostgreSQL, MySQL, NoSQL (CosmosDB, MongoDB. Azure Data Factory, Airflow oder andere ETL/ELT Tools, Python (Pandas), SQL, PowerBI (DAX), Tableau, Qlik, Looker, KI / AI.
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Experience must involve one (1) year of experience in the following: data pipelines, ETL (Extract, Transform, Load); data analysis; dashboard and visualization; data modeling; API; Pandas; Python; SQL; Java; MySQL; and MongoDB. Telecommuting permitted.
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In-depth working knowledge and administration of structured and unstructured data: RDS and Oracle; MySQL, PostgreSQL, or SQL Server; DynamoDB and/or MongoDB is a plus. Implement ETL and data conversion, using an industry standard ETL tools.
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1-2 years experience in working with databases, particularly PostgreSQL, MongoDB, and familiarity with MS Access, IBM Lotus (for legacy data). Good knowledge of ETL technologies, ideally AWS/Azure services and SSIS (SQL server Integration Services) but knowledge of AWS is also sufficient.
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Experience in using Jupyter notebooks and one or more statistical visualization or graphing toolkits such as Excel, Qlik Sense or Tableau. The Senior Data Scientist at Sensor Tower will leverage digital advertising along with mobile, desktop, and OTT industry knowledge to drive strategies that improve the accuracy and offerings for our Digital Marketing insights solution.
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Familiarity with Databricks for data engineering tasks including ETL processes, data visualization, and machine learning. Knowledge of relational and NoSQL databases such as MySQL, PostgreSQL, MongoDB, etc.
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Fortes compétences en composants d’intégration (API, Event, ETL, Job) : Kong, Kafka, Mulesoft, Solace, ODI, BODS, EDIFACT, SBI, MFT. Bonnes connaissances des bases de données : relationnelle et non relationnelle (Oracle SQL Server, Postgresql, Mongodb, redis, Snowflake, singlestore.
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Performs hands-on design and development with PySpark/Pandas/SQL, AWS Glue for ETL and Catalog, Datasync, Parquet files, Warehousing experience preferably with Snowflake and/or Redshift. Experience with ETL tools and frameworks (e.g., Apache NiFi, Talend, AWS Glue, Azure Data Factory.
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Experience with front-end development using HTML, CSS, and JavaScript frameworks (e.g., React, Angular, Vue.js). The ideal candidate will have a solid understanding of Java and Python programming languages, along with experience working with Databricks for data engineering tasks.
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Data Engineering experience including ETL processes, data warehousing, and big data technologies (e.g., Hadoop, Spark). Solid understanding of database technologies, both SQL (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, DynamoDB), including schema design, query optimization, and connection pooling.
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Python (Jupyter), R, Elasticsearch, Non-Relational DBs: MongoDB / Cassandra, Relational DBs: MySQL, SingleStore, Presto and Docker containers. ESSENTIAL DUTIES AND RESPONSIBILITIES:Develop and maintain software applications, Customized Data Decoders (ETL: Extraction, Transformation, Loading), web-based data analytics & visualization applications and machine learning models for machine-generated data sets.
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Deep knowledge and experience with modern database technologies, platforms, and solutions including, but not limited to S3, Snowflake, Python, Postgres, RDS, NoSql databases, MongoDB, DynamoDB, etc.
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