Data Engineer
Posted on 9/15/2023
INACTIVE
Effectual

201-500 employees

AWS managed cloud services provider
Company Overview
Effectual, an AWS Premier Consulting Partner, stands out for its robust cloud-first managed and professional services, assisting both commercial enterprises and public sector entities in risk mitigation and IT modernization. The company's strength lies in its experienced team that applies proven methodologies to business challenges across Amazon Web Services and VMware Cloud on AWS, backed by private equity firms Catalyst Investors and Lumerity Capital. Furthermore, Effectual's commitment to security is demonstrated by its membership in the Cloud Security Alliance and the PCI Security Standards Council, while its adoption of Generative AI in financial services positions it at the forefront of technological advancements in the industry.
Consulting

Company Stage

Private

Total Funding

N/A

Founded

2018

Headquarters

Jersey City, New Jersey

Growth & Insights
Headcount

6 month growth

19%

1 year growth

27%

2 year growth

35%
Locations
Remote
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Apache Spark
AWS
BigQuery
Apache Kafka
Data Analysis
Data Structures & Algorithms
Hadoop
Java
Airflow
Microsoft Azure
Redshift
SQL
Talend
Apache Flink
Python
NoSQL
CategoriesNew
Data & Analytics
Requirements
  • MUST HAVE hands-on experience with Confluent Kafka including both administration and development
  • Either Confluent Certified Administrator for Apache Kafka (CCAAK) or Confluent Certified Developer (CCDAK) for Apache Kafka certificates, however, both are preferred
  • Bachelor's or master's degree in computer science, Engineering or a related field
  • 5+ years of experience working as a Data Engineer in an AWS professional services or consulting environment
  • Proficiency in programming languages such as Python, Java, or Scala, with expertise in data processing frameworks and libraries (e.g., Spark, Hadoop, SQL)
  • In-depth knowledge of database systems (relational and NoSQL), data modeling, and data warehousing concepts
  • Strong knowledge of data architectures and data modeling and data infrastructure ecosystem
  • Experience with cloud-based data platforms and services (e.g., AWS, Azure) including familiarity with relevant tools (e.g., S3, Redshift, BigQuery, etc.)
  • Proficiency in designing and implementing ETL processes and data integration workflows using tools like Apache Flink, Apache Airflow, Informatica, or Talend
  • Familiarity with data governance practices, data quality frameworks, and data security principles
  • Work minimal direction and turn a clients want and need into working stories, epics which can be performed upon during a sprint
  • A firm understanding of the SDLC process
  • An understanding of object-oriented programming
  • The ability to communicate cross-functionally, derive requirements and architect shared datasets; ability to synthesize, simplify and explain complex
  • The ability to thrive in a dynamic environment. That means being flexible and willing to jump in and do whatever it takes to be successful
  • Ability to travel, with strong preference to mid-west time zone or east coast
  • Knowledge of batch and streaming data architectures
  • Product mindset to understand business needs and come up with scalable engineering solutions
  • AWS Certified Cloud Practitioner
  • AWS Certified Data Analytics Specialty
  • AWS Certified Machine Learning Specialty
  • AWS Certified Database Specialty
  • SnowPro Core Certification
  • Databricks Certified Data Engineer Associate
Responsibilities
  • Design, build and launch extremely efficient and reliable data pipelines, utilizing Confluent Kafka, to move data across several platforms including Data Lakes, Data Warehouses, and real-time systems
  • Develop, construct, test and maintain data architectures from the data architect
  • Analyze organic and raw data
  • Build data systems and pipelines
  • Build the infrastructure required for extraction, transformation, and loading of data from different data sources using SQL and AWS 'big data' technologies
  • Write scripts for data architects, data scientists, and data quality engineers
  • Data acquisition
  • Identify ways to improve data reliability, efficiency, and quality
  • Develop dataset processes
  • Prepare data for prescriptive and predictive modeling
  • Automate the data collection and analysis processes, data releasing and reporting tools
  • Build algorithms and prototypes
  • Develop analytical tools and programs