Data Engineer (AWS, Azure, GCP)

CapTech is an award-winning consulting firm that collaborates with clients to achieve what’s possible through the power of technology. At CapTech, we’re passionate about the work we do and the results we achieve for our clients. From the outset, our founders shared a collective passion to create a consultancy centered on strong relationships that would stand the test of time. Today we work alongside clients that include Fortune 100 companies, mid-sized enterprises, and government agencies, a list that spans across the country.

CapTech Data Engineering consultants enable clients to build and maintain advanced data systems that bring together data from disparate sources in order to enable decision-makers. We build pipelines and prepare data for use by data scientists, data analysts, and other data systems. We love solving problems and providing creative solutions for our clients. Cloud Data Engineers leverage the client’s cloud infrastructure to deliver this value today and to scale for the future. We enjoy a collaborative environment and have many opportunities to learn from and share knowledge with other developers, architects, and our clients.

Skills

Successful candidates usually have demonstrable experience with technologies in some of these categories:

Responsibilities

Developing data pipelines and other data products using Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP); Advising clients on specific technologies and methodologies for utilizing cloud resources to efficiently ingest and process data quickly; Utilizing your skills in engineering best practices to solve complex data problems; Collaborating with end users, development staff, and business analysts to ensure that prospective data architecture plans maximize the value of client data across the organization.; Articulating architectural differences between solution methods and the advantages/disadvantages of each

Requirements

Experience delivering solutions on a major cloud platform; Ability to think strategically and relate architectural decisions/recommendations to business needs and client culture; Experience in the design and implementation of data architecture solutions; A wide range of production database experience, usually including substantial SQL expertise, database administration, and scripting data pipelines; Ability to assess and utilize traditional and modern architectural components required based on business needs.; A demonstrable ability to deliver production data pipelines and other data products. This could be hands on experience, degree, certification, bootcamp, or other learning.; Languages: SQL, Python, Java, R, C# / C++ / C; Database: SQL Server, PostgreSQL, Snowflake, Redshift, Aurora, Presto, BigQuery, Oracle; DevOps: git, docker, subversion, Kubernetes, Jenkins; Additional Technologies: Spark, Databricks, Kafka, Kinesis, Hadoop, Lambda, EMR; Popular Certifications: AWS Cloud Practitioner, Microsoft Azure Data Fundamentals, Google Associate Cloud Engineer