Senior Data Engineer

Job Requisition ID #

26WD100084

Position Overview

We are seeking an experienced Senior Data Engineer with 7+ years of expertise in designing, building, and optimizing scalable data platforms and pipelines. In this role, you will architect and develop robust data solutions that enable analytics, machine learning, and business intelligence across the organization.

You will collaborate closely with Data Science, Machine Learning Engineering, Platform Engineering, Product, Analytics, and business stakeholders to build reliable, high-performance data ecosystems that deliver trusted, actionable insights. This role requires strong technical leadership, hands-on engineering expertise, and the ability to influence data architecture and engineering best practices.

Our culture emphasizes collaboration, innovation, continuous learning, and engineering excellence. We encourage ownership, knowledge sharing, and solving complex technical challenges while mentoring fellow engineers.

Key Responsibilities

• Design, develop, and maintain scalable, resilient, and high-performance data pipelines supporting batch and real-time workloads
• Build and optimize ETL/ELT pipelines for ingesting, transforming, validating, and serving large-scale datasets
• Design and implement robust data models, schemas, and storage strategies to ensure data quality, integrity, and accessibility
• Develop and optimize distributed data processing solutions using Spark, PySpark, SQL, and cloud-native technologies
• Integrate data from multiple enterprise systems while maintaining consistency, governance, and reliability
• Build and manage scalable data platforms using modern cloud services and data lake technologies
• Optimize database, warehouse, and pipeline performance for large-scale analytical workloads
• Implement monitoring, alerting, automated testing, and data quality validation across pipelines
• Establish and enforce data governance, security, compliance, and engineering best practices
• Partner with Product, Analytics, Data Science, Machine Learning, and Engineering teams to translate business requirements into scalable data solutions
• Enable self-service analytics by delivering trusted, well-modeled datasets
• Contribute to the technical roadmap by driving architecture discussions, defining engineering standards, and promoting reusable data platform components
• Mentor junior engineers through code reviews, technical guidance, and best practice adoption
• Maintain comprehensive technical documentation covering architecture, data models, workflows, and operational processes

• Bachelor's degree in computer science, Information Technology, Engineering, or a related field (or equivalent practical experience)
• 7+ years of professional experience in Data Engineering, Data Platform Engineering, or Distributed Data Systems
• Strong hands-on expertise in:
  
  
  • Python
  • Spark / PySpark
  • Advanced SQL
  • Shell scripting
• Strong understanding of relational databases such as PostgreSQL and MySQL
• Experience working with NoSQL databases including MongoDB, Cassandra, DynamoDB, or equivalent
• Solid understanding of data modeling, data architecture, and warehouse design principles
• Experience designing and operating scalable ETL/ELT pipelines across batch and streaming environments
• Hands-on experience with modern data technologies such as:
  
  
  • Apache Kafka
  • Apache Flink
  • Apache Iceberg
  • Hive
  • Parquet
• Experience using workflow orchestration platforms such as Apache Airflow
• Strong experience with cloud platforms, preferably AWS, including:
  
  
  • EMR
  • Glue
  • S3
  • IAM
  • Lambda
  • Step Functions
  • Athena
  • Redshift
• Experience with modern data warehouse platforms such as Snowflake, Amazon Redshift, or Google BigQuery
• Experience implementing CI/CD pipelines, version control using Git, and infrastructure automation
• Experience implementing data validation, monitoring, incremental processing, backfills, reconciliation, and operational support for production pipelines
• Proven ability to lead technical initiatives, influence architecture decisions, establish engineering standards, and mentor engineering teams

• Experience with modern data transformation and ingestion tools such as dbt, Fivetran, Airbyte, or similar
• Experience building enterprise-scale data platforms supporting analytics, AI, and machine learning workloads
• Experience collaborating with Product Management, Design, Research, Analytics, and Machine Learning teams to build data products
• Familiarity with machine learning workflows, feature engineering, and MLOps concepts
• Experience in customer analytics, personalization, recommendation systems, or digital optimization
• Knowledge of real-time streaming architectures and event-driven data processing
• Experience working with Data Lakehouse architectures and open table formats
• Strong understanding of data governance, metadata management, lineage, and data catalog solutions
• Excellent communication, stakeholder management, and cross-functional collaboration skills
• Passion for continuous improvement, engineering excellence, and mentoring high-performing teams