Data Engineer (SQL, Python & AWS) (Remote)
- Job Title
- Data Engineer (SQL, Python & AWS) (Remote)
- Job ID
- Orlando, FL
- Other Location
From our start in 2009, Conexess has established itself in 3 markets, employing nearly 200+ individuals nation-wide. Operating in over 15 states, our client base ranges from Fortune 500/1000 companies to mid-small range companies. For the majority of the mid-small range companies, we are exclusively used due to our outstanding staffing track record.
Who We Are:
Conexess is a full-service staffing firm offering contract, contract-to hire, and direct placements. We have a wide range of recruiting capabilities extending from help desk technicians to CIOs. We are also capable of offering project-based work.
Conexess Group is aiding a large healthcare client in their search for a Data Engineer in a remote capacity. This is a long-term opportunity with a competitive compensation package.
******We are unable to work C2C on this role******
- Designs and develops a consolidated, conformed enterprise data warehouse and data lake which store all critical data across Customer, Provider, Claims, Client and Benefits data.
- Manages processes that are highly complex and impact the greater organization.
- Designs, develops and implements methods, processes, tools and analyses to sift through large amounts of data stored in a data warehouse or data mart to find relationships and patterns.
- May lead or manage sizable projects. Participates in the delivery of the definitive enterprise information environment that enables strategic decision-making capabilities across enterprise via an analytics and reporting.
- Focuses on providing thought leadership and technical expertise across multiple disciplines.
- Experience developing productionized database pipelines using SQL, Spark-SQL, Postgres-SQL, No-SQL queries across large volumes of data within Amazon Web Service (AWS) services and infrastructure S3, Delta lake, Lambda, Glue, Terraform, EC2/ASG, SQS/SNS, ECS,
- Automated CI/CD frameworks such as Apache Airflow, Jenkins, seed entries and AB testing.
- Advanced knowledge of performing ETL aggregations and loads across partitioned data frames in Python using PySpark, Data Bricks, Domino Jupyter notebooks, QueryGrid, Snowpipe, Kafka/Qlik, Hue, Superset, Impala
- Strong knowledge of data structures, algorithms, time complexity and advanced Object-Oriented Design with Python.
- Developed Spark code using Scala and Spark-SQL/Streaming for faster testing and processing of data.
- Experience interacting with large data warehouses in the AWS cloud including Teradata Vantage, Snowflake, Hadoop, HDFS
- Developed dashboards for visualizations and enhanced data monitoring using Cloud Watch, Prometheus, Graphana, Kibana