This role will provide expertise to support the development of a Big Data / Data Lake system architecture that supports enterprise data operations for the District of Columbia government, including the Internet of Things (IoT) / Smart City projects, enterprise data warehouse, the open data portal, and data science applications.

Job Title: Data Engineer/ Data ArchitectLocation: Corvallis, OR – 5 Days Onsite Role Job Description: At least 8 to 10+ years’ experience in data engineering , AI and ML and Aws/Azure Architect, Design and implement distributed data processing pipelines using Spark, Python, SQL and other tools and languages prevalent in the Big Data/Lakehouse ecosystem.

The big data architect will support the development of autonomous driving software solutions by playing the role of subject matter expert for the big data systems, underlying data and data products.

Advanced technical certifications: AWS Certified Cloud Practitioner, Solutions Architect, Certified Developer, or SysOps Administrator certifications; RHEL RHCSA/RHCE; AWS Certified Data Analytics, DASCA Big Data Engineering and Analytics preferred.

As a big data engineer at Booz Allen, you'll implement data engineering activities on some of the most mission-driven projects in the industry. We need an experienced data engineer like you to help our clients find answers in their big data to impact important missions—from fraud detection to cancer research to national intelligence.

Utilize Databricks for big data processing and streaming analytics. Experience with big data technologies such as Apache Hadoop, Spark, Kafka, and others. The ideal candidate will have a robust background in big data analytics, data engineering, and cloud computing, with a particular focus on building and optimizing data lakes on AWS. This role is crucial for designing, implementing, and managing advanced data solutions using AWS and Data Lakehouse platforms.

At least 4 years of experience in data engineering working with Big Data Technologies: Apache Spark, Pyspark, Hadoop, and DataBricks with delta lake. Requires a minimum of a bachelor's or foreign equivalent degree in Management Information Systems, Computer Science and Engineering, or a related field plus 4 years of experience as a software developer, data engineer, or related occupation.

Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is a plus. Your expertise in Airflow, DBT, and Databricks will be essential in optimizing our data workflows and enabling advanced analytics and insights.

In HADOOP , you'll be given an introduction to BIG DATA, APACHE HADOOP, HADOOP ECOSYSTEM, CLOUDERA QUICKSTART VM along with core concepts of the Hadoop framework including MapReduce, HIVE, PIG, SQOOP, FLUME, HBASE, OOZIE.

Strong experience with distributed data architectures and big data processing technologies (Spark, EMR, Hadoop, Hive) Discerning appreciation of best practices in designing data lakes/lake houses, mentoring junior members, peer review and recommendation.

Plant monitoring and care during vegetative and flowering phase: trimming and staking; big leafing and pruning; plant care, watering and feeding; plant monitoring and climate control; pest and disease control; inventory data input.

Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage. Utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics.

Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc. Internal knowledge of open source or related big data technologies. As a member of the Data Engineering Web3 team, you own the ETL/ELT pipelines and data warehouse that are used to analyze trends and activity in our Web3 product offerings.

What you'll bring to Circle: For Senior Data Engineer (III) 4+ years of professional data engineering experience. Designing, building and maintaining data ETL/ELT pipelines to source and aggregate the required data for various data analyses and reporting needs, as well as to continually improve the operations, monitoring and performance of the data warehouse.

6+ years of experience in data engineering working with big data systems that can process and transform data at scale. Experience developing with data SaaS and BI tooling (Fivetran, Looker, dbt, Databricks, Apache Airflow, etc.