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Coda Search│Staffing is seeking a Data Engineer to analyze large business data, build scalable data pipelines, and drive insights across Jira, Portal, and Salesforce. You will partner with product managers, software engineers, and BI engineers to automate analytics and deliver actionable metrics at scale.
You will own large datasets, implement pipelines, and help shape BI infrastructure while improving reporting processes and data security practices.
As a Data Engineer, you are responsible for analyzing large amounts of business data, solving real world problems, and developing metrics and business cases that will enable Business Insights. This is done by leveraging data from various platforms such as Jira, Portal, Salesforce. You will work with a team of Product Managers, Software Engineers and Business Intelligence Engineers to automate and scale the analysis, and to make the data more actionable to manage business at scale. You will own many large datasets, implement new data pipelines that feed into or from critical data systems.A career with purpose.
Bachelor's degree in Computer Science, Information Technology, or a related field.
3-5 years of experience in data engineering or a related role, with demonstrated success in delivering data solutions.
Proficient in SQL, with the ability to write complex queries, perform query optimization, and conduct performance tuning.
Experience with NoSQL databases, such as MongoDB, Cassandra, or DynamoDB, and an understanding of their appropriate use cases.
Strong programming skills in Python, Java, or Scala, with experience in data processing frameworks (e.g., Apache Spark, Hadoop).
Experience with cloud platforms (AWS, Azure, GCP) and data services, such as AWS Redshift, Azure Synapse, or Google BigQuery.
Knowledge of big data technologies, including Hadoop, Spark, Kafka, and HBase, with experience in distributed data processing.
Familiarity with data orchestration tools, such as Apache Airflow for scheduling and managing data workflows.
Experience with data versioning and testing tools, such as DVC (Data Version Control) and dbt (data build tool).
Understanding of data security practices, including encryption, access controls, and data masking.