Sr Data Engineer

Tailored Brands•Houston, TX

58d•Hybrid

About The Position

At Tailored Brands, we help people love the way they look and feel for their most important moments. Our Technology team loves the way they feel and thrive at work, with: Flexible work opportunities, including remote and hybrid options, Small, empowered teams that have fun delivering real value for our customers, A culture that values a 50-year legacy while eagerly embracing the future. Want to be part of this? We currently have an exciting opportunity for Sr Data Engineer to join our Tailored Technology team. This individual will assist with assessing the design, development, and maintenance of data infrastructure and pipelines within the organization. These professionals are responsible for ensuring the reliability, scalability, and efficiency of data systems, as well as optimizing data workflows for performance and usability. They often collaborate with cross-functional teams to understand data requirements, architect solutions, and implement best practices for data management, processing, and storage. They also play a crucial role in identifying opportunities for automation, streamlining processes, and implementing cutting-edge technologies to drive innovation in data-driven decision-making. Additionally, they may mentor and provide technical guidance to junior team members, participate in strategic planning, and contribute to the overall data strategy of the organization.

Requirements

Bachelor's or Master's degree in Computer Science,or AI Engineering, or a related field.
8+ years of experience in a data engineering or platform role, ideally working with large, complex datasets; retail industry experience a plus.
Strong understanding of software engineering best practices, including agile development, coding standards, code reviews, testing, and operational support.
Hands-on experience designing, building, and enhancing cloud-based data warehouse and ETL/ELT solutions, including integration with Application Programming Interfaces (APIs) and external data sources.
Proven ability to develop scalable, accurate, and timely data pipelines as data volume and complexity grow.
Proficiency in engineering and automating batch and streaming data pipelines, including data mappings and transformations.
Strong experience with cloud platforms inluding Snowflake,bigquery on AWS and GCP, with proficiency in SQL, and scripting/programming languages such as Python, PySpark, and Bash.
Strong experience optimizing Snowflake performance using query profiling, virtual warehouse tuning, clustering keys, Search Optimization Service, Query Acceleration Service, caching, and cost optimization best practices.
Strong experience with data modeling, data warehousing environments, and ETL/ELT tools such as Fivetran, HVR, or similar platforms.
Strong experience writing complex SQL queries and optimizing performance in Snowflake and relational databases such as Snowflake, bigquery,Oracle or MySQL.
Strong experience of CI/CD practices and tools (e.g., GitHub, Jenkins, Kubernetes, Ansible) for production deployments.
Experience supporting DSAR and regulatory compliance (GDPR/CCPA) using Snowflake data governance, masking policies, RBAC, data lineage, and secure data access controls
Ability to independently troubleshoot and resolve complex production and data quality issues.
Strong experience supporting analysts and data scientists with query optimization, performance tuning, and data processing.
Strong communication and interpersonal skills, with the ability to collaborate effectively across technical and non-technical teams.
A proactive mindset with the ability to identify improvement opportunities and present recommendations to leadership.

Nice To Haves

Strong experience with workflow orchestration and scheduling tools such as Airflow or Automic UC4 is a plus.
Experience implementing data governance using Snowflake Horizon or Atlan, RBAC, masking policies, data lineage, metadata management, and enterprise security best practices.
Experience implementing data quality frameworks using automated validation, monitoring, reconciliation, and observability tools such as Anamolo, Great Expectations, or custom SQL/Python checks.
Exposure to business intelligence tools such as power bI,essbase or MicroStrategy is advantageous.

Responsibilities

Design, assess and support efficient, reliable data pipelines that move data across enterprise systems including Snowflake, BigQuery,Oracle, AS400, SQL Server, Salesforce and external partner platforms using API’s, AWS S3, and GCP buckets.
Enhance and scale cloud-based data warehousing and ETL capabilities to automate large-scale data ingestion and transformation across diverse data sources.
Identify opportunities to automate, optimize, and modernize existing data processes to improve performance, reliability, and productivity.
Own the solutions and designs you build, ensuring they meet operational standards, performance expectations, and long-term scalability.
Contribute to strong data operations environment by adhering to SLAs, improving data quality, and implementing monitoring and automation best practices.
Collaborate closely with data scientists, analytics teams, and business partners to deliver data solutions that support reporting, analytics, and predictive use cases.
Mentor and coach junior engineers by providing technical guidance, design feedback, and supporting production operations.
Adapt quickly to changing business priorities and data needs while meeting delivery timelines and supporting production operations.