Senior Data Engineer

Overview

Remote
$140,000 - $160,000
Full Time

Skills

Confluent
Data Lake
Data Migration
Data Modeling
DataFrames
ETL
Indexing
JSON
Kafka
Pentaho
Postgres
Pytest
Python
RDBMS
SSIS
Spark DataFrames
Unittest
big data
containerization
data architecture
data architectures
data lakes
data pipeline
data pipelines
data pipelinesCreate
data validation
data warehouse
enterprise data
lifecycle
performance tuning
quality
system integration
transformation
transformationsMaintain

Job Details

Location: 100% Remote

Years Experience: 10+ years

Education: Bachelor s in IT related field

Work Authorization: Must show that applicant is legally permitted to work in the United States.

Clearance: Applicants must be able to meet the requirements to obtain an Public Trust security clearance. NOTE: United States Citizenship is required to be eligible to obtain this security clearance.

Key Skills:

  • 10+ years of IT experience focusing on enterprise data architecture and management
  • Experience with Databricks required
  • 8+ years experience in Conceptual/Logical/Physical Data Modeling & expertise in Relational and Dimensional Data Modeling
  • Experience with Great Expectations or other data quality validation frameworks
  • 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)
  • Experience with AWS environment, CI/CD pipelines, and Python (Python 3) a bonus

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

  • 10+ years of IT experience focusing on enterprise data architecture and management
  • 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 bonus
  • Experience working with JSON and defining JSON Schemas a bonus
  • Experience setting up and management Confluent/Kafka topics and ensuring performance using Kafka a bonus
    • Familiarity with Schema Registry, message formats such as Avro, ORC, etc.
    • Understanding how to manage ksqlDB SQL files and migrations and Kafka Streams
    • Ability to thrive in a team-based environment
      • Experience briefing the benefits and constraints of technology solutions to technology partners, stakeholders, team members, and senior level of management