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*your impact*:*:*what you'll work on*:- highly scalable and finely crafted data pipelines- business intelligence and data analytics capabilities to drive low-latency key performance indicator generation; - custom connectors to extract critical data from third party systems for ingestion into the enterprise data warehouse; - event bus and event sourcing capabilities that provide business and engineering leverage and efficiencies; - transactional or eventually consistent stores that provide well-encapsulated domain object semantics; - orchestrated scale-outdata pipelines that can leverage serverless and containerized compute that balance cost, latency, and duration; - algorithmically intensive data engines that operate on streaming, large, or multi-tenant datasets; - innovative data validation frameworks to enhance data quality and reliability.
*who you are*:- 5+ years experience as a data engineer or in a similar role- strong sql skills and knowledge of modern enterprise programming languages like python, java, or c#- excellent with data modeling, database design and data analysis, including how to scale out, make highly available, or map to storage systems- strong experience with data infrastructure/database technologies such as: snowflake, redshift, vertica, teradata, oracle rac, postgresql, informatica- able to employ modern continuous integration and continuous deployment (ci/cd) tools with an emphasis on a well-maintained testing pyramid- experience with business intelligence technologies such as quicksight, tableau, qlik, or looker- experience with 'big data' technologies such as kafka, dataflow, and spark and data processing and orchestration systems such as dbt, nifi, and airflow,- extensive experience designing and operating software in a cloud provider such as aws or gcp*even better if you are...*:- experienced translating requirements into a robust, scalable data pipelines to support dynamic workloads and world-class analytics- skilled taking technical or business context to guide the team by turning ambiguity into clarity- experienced with persistence mechanisms storage technologies such as; relational databases, nosql stores, data caches, etc.
*:*disco's technology stack*:enterprise reporting: snowflake, quicksight, fivetran, dbtcloud provider: aws - ecs, ec2, lambda, aurora mysql, redshift, dynamodb, sqs, sns, kinesis, s3, cloudfront, cloudformation, sagemaker, kms, codepipeline, etc.event bus: kafka and schema registryci/cd: terraform, docker (via ecs), jenkins, codedeploy, github, artifactory, consul for app config, service discovery, shared secretsvisibility: elk stack for logging, data dog, new relic, sentry.ioprogramming languages: java/kotlin, python/flask, c#/