Sr. Data Engineer

OverviewPyramid Systems is looking for a Data Engineer (Senior) who is passionate about bringing creative architect solutions to end customers.Key Skills: 8+ years of IT experience focusing on enterprise data architecture and management Experience with Databricks, Structured Streaming, Delta Lake concepts, and Delta Live Tables required Experience with ETL and ELT tools such as SSIS, Pentaho, and/or Data Migration Services Advanced level SQL experience (Joins, Aggregation, Windowing functions, Common Table Expressions, RDBMS schema design, Postgres performance optimization) Responsibilities Plan, create, and maintain data architectures, ensuring alignment with business requirements Obtain data, formulate dataset processes, and store optimized data Identify problems and inefficiencies and apply solutions Determine tasks where manual participation can be eliminated with automation. Identify and optimize data bottlenecks, leveraging automation where possible Create and manage data lifecycle policies (retention, backups/restore, etc) In-depth knowledge for creating, maintaining, and managing ETL/ELT pipelines Create, maintain, and manage data transformations Maintain/update documentation Create, maintain, and manage data pipeline schedules Monitor data pipelines Create, maintain, and manage data quality gates (Great Expectations) to ensure high data quality Support AI/ML teams with optimizing feature engineering code Expertise in Spark/Python/Databricks, Data Lake and SQL Create, maintain, and manage Spark Structured Steaming jobs, including using the newer Delta Live Tables and/or DBT Research existing data in the data lake to determine best sources for data Create, manage, and maintain ksqlDB and Kafka Streams queries/code Data driven testing for data quality Maintain and update Python-based data processing scripts executed on AWS Lambdas Unit tests for all the Spark, Python data processing and Lambda codes Maintain PCIS Reporting Database data lake with optimizations and maintenance (performance tuning, etc) Streamlining data processing experience including formalizing concepts of how to handle lake data, defining windows, and how window definitions impact data freshness. Qualifications 8+ years of IT experience focusing on enterprise data architecture and management Must be able to obtain a Public Trust security clearance MUST BE US CITIZEN Bachelor degree required Experience in Conceptual/Logical/Physical Data Modeling & expertise in Relational and Dimensional Data Modeling Experience with Databricks, Structured Streaming, Delta Lake concepts, and Delta Live Tables required Additional experience with Spark, Spark SQL, Spark DataFrames and DataSets, and PySpark Data Lake concepts such as time travel and schema evolution and optimization Structured Streaming and Delta Live Tables with Databricks a bonus Experience leading and architecting enterprise-wide initiatives specifically system integration, data migration, transformation, data warehouse build, data mart build, and data lakes implementation / support Advanced level understanding of streaming data pipelines and how they differ from batch systems Formalize concepts of how to handle late data, defining windows, and data freshness Advanced understanding of ETL and ELT and ETL/ELT tools such as SSIS, Pentaho, Data Migration Service etc Understanding of concepts and implementation strategies for different incremental data loads such as tumbling window, sliding window, high watermark, etc. Familiarity and/or expertise with Great Expectations or other data quality/data validation frameworks a bonus Understanding of streaming data pipelines and batch systems Familiarity with concepts such as late data, defining windows, and how window definitions impact data freshness Advanced level SQL experience (Joins, Aggregation, Windowing functions, Common Table Expressions, RDBMS schema design, Postgres performance optimization) Indexing and partitioning strategy experience Debug, troubleshoot, design and implement solutions to complex technical issues Experience with large-scale, high-performance enterprise big data application deployment and solution Understanding how to create DAGs to define workflows Familiarity with CI/CD pipelines, containerization, and pipeline orchestration tools such as Airflow, Prefect, etc a bonus but not required Architecture experience in AWS environment a bonus Familiarity working with Kinesis and/or Lambda specifically with how to push and pull data, how to use AWS tools to view data in Kinesis streams, and for processing massive data at scale a bonus Experience with Docker, Jenkins, and CloudWatch Ability to write and maintain Jenkinsfiles for supporting CI/CD pipelines Experience working with AWS Lambdas for configuration and optimization Experience working with DynamoDB to query and write data Experience with S3 Knowledge of Python (Python 3 desired) for CI/CD pipelines a bonus Familiarity with Pytest and Unittest a bon