Big Data Engineer

Key Responsibilities

• Build, run, and own ETL pipelines on EMR (Spark) orchestrated in Airflow - including their monitoring, alerting, and recovery.
• Write SQL and Python for data ingestion, transformation, and the datasets that analysts and dashboards depend on.
• Own data quality for what you build - run the checks before you ship and add new ones where they're missing; confirm the results are correct, not only that the job completed.
• Build and extend the data models for your area: fact and dimension tables following the team's layering conventions.
• Keep your jobs efficient - watch runtime and cost, and raise slow or expensive jobs rather than living with them.
• Use and extend the team's shared patterns and templates, and turn work you find yourself repeating into something automated or reusable.
• Work with analysts and business stakeholders to turn requests into datasets that actually get used.

Required Qualifications

• 2-4 years of hands-on data development in a production environment - pipelines that run on a schedule with real downstream consumers, that you were responsible for including when they broke.
• Strong SQL: window functions, complex multi-table joins, incremental loads, and the ability to work out why a query is slow.
• Python for production ETL and tooling (PySpark, pandas, boto3) - code that runs on a schedule, not only notebooks.
• Hands-on experience with Spark on a cloud platform: AWS EMR, Databricks, Glue, or equivalent.
• Production experience with a scheduler, Airflow preferred: DAG design, dependencies, retries, and reruns that are safe to repeat.
• Working understanding of dimensional modeling: fact and dimension tables, star schema, and warehouse layering.
• Comfortable working on AWS (S3 with Parquet / ORC), Linux, and Git - and comfortable picking up new tools as the platform evolves.
• Effective use of AI to solve data problems - using it to move faster on SQL, debugging, and unfamiliar schemas, with the judgment to catch output that looks right but isn't.
• Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.

Preferred Qualifications

• Data ingestion or CDC tooling: DataX, Sqoop, Debezium, Fivetran, or similar.
• An OLAP / MPP engine: StarRocks, Doris, ClickHouse, Redshift, or similar.
• Spark performance work: partitioning, shuffle, skew, and memory tuning.
• AWS cost optimization: EMR instance sizing and Spot strategy, S3 lifecycle policies.
• Lakehouse formats (Iceberg, Hudi, Delta), streaming (Kafka, Flink), dbt, or a data quality framework.
• QuickSight or another BI tool: dataset and permission design.
• Self-directed learning: a project, an open-source contribution, or a tool you picked up on your own and put to real use

Benefits & Perks

Base Salary Range: $100 - 120K

• Free snacks and drinks
• Fully paid medical, dental, and vision insurance (partial coverage for dependents)
• Contributions to 401K funds
• Bi-annual reviews, and annual pay increases
• Health and wellness benefits, including free gym membership
• Quarterly team-building events

• At TP-Link Systems Inc., we are continually searching for ambitious individuals who are passionate about their work.
• We believe that diversity fuels innovation, collaboration, and drives our entrepreneurial spirit.
• As a global company, we highly value diverse perspectives and are committed to cultivating an environment where all voices are heard, respected, and valued.

• Beyond compliance, we strive to create a supportive and growth-oriented workplace for everyone. If you share our passion and connection to this mission, we welcome you to apply and join us in building a vibrant and inclusive team at TP-Link Systems Inc.
• Please, no third-party agency inquiries, and we are unable to offer visa sponsorships at this time.