AI Staff Engineer (Addison, TX)

TEKsystems; Addison (TX); On-site; USD 175,000 - 200,000; Full time

Benefits offered by this job

Unlimited PTO

Volunteer day

Bonus eligibility

Stock purchase plan

Health benefits

401k matching

Education assistance

Job summary

TEKsystems is seeking a senior data engineering leader to own the architecture and evolution of a global data platform spanning transactional, streaming, and analytical workloads. You will design and run scalable AWS-based data systems (Aurora PostgreSQL, Redis) and enable efficient data movement to Snowflake and other analytics platforms.

You will drive data governance, modeling standards, and observability across distributed systems, collaborating with DevOps, product, and business teams to

Qualifications

• Experience designing data platforms across transactional, streaming, and analytical workloads.
• Strong AWS data tooling experience (Aurora PostgreSQL, Redis) and data governance practices.
• Proficiency with data modeling, schemas, and zero-copy techniques.
• Ability to lead complex cross-team data initiatives and mentor engineers.

Responsibilities

• Own architecture and evolution of the data platform across transactional, streaming, and analytical systems.
• Design and operate scalable data systems on AWS and integrate with Snowflake.
• Improve observability, monitoring, and incident response for data pipelines.
• Partner with DevOps, product, and business teams to translate data needs into scalable solutions.

Skills

Python

AWS

Snowflake

Airflow

Golang

Scala

ML Ops

Dagster

Prefect

Immuta

Debezium

Terraform

Tools

Airflow

Dagster

Prefect

Immuta

Great Expectations

Soda

Job description

Description

What Youll Need Own the architecture and evolution of the data platform across transactional, streaming, and analytical systems Design and operate scalable data systems on AWS, including Aurora PostgreSQL, Redis, and event-driven pipelines Build and optimize data flows between operational systems and analytics platforms (e.g., Snowflake, observability and product analytics tools) Ensure data integrity, consistency, and availability across distributed systems Design for high-throughput, low-latency data processing supporting real-time and near-real-time use cases Establish data modeling standards, governance practices, and access patterns across the organization Drive improvements in data observability, monitoring, and incident response Partner with DevOps, product, and business teams to translate data needs into scalable technical solutions Lead complex initiatives such as data correlation across systems, unified event models, and cross-platform insights Identify and resolve bottlenecks, scaling challenges, and data quality issues Mentor engineers and elevate data engineering practices across the organization 1. Transactional & In-Memory Data Storage (Core Ecosystem) AWS Aurora PostgreSQL: Serves as the primary operational database managing digital commerce and transactional order context. Code/Practices: Advanced PL/pgSQL, connection pooling optimization, partition schema design for transactional data, tuning vacuum settings, and index strategy optimization Redis: Leveraged as the distributed in-memory cache and state manager to support low-latency requirements on high-volume consumer apps. Code/Practices: Key-value eviction strategies, cluster configuration, data structure caching routines (Hashes, Sorted Sets), and handling distributed cache-stampede mitigation. Flyway / Liquibase: Database schema migration and version control tooling utilized natively to evolve transactional database architecture alongside continuous app development.

2. High-Throughput Streaming & Event-Driven Processing AWS Kinesis / Apache Kafka: Infrastructure underpinning real-time and near-real-time streaming applications. Code/Practices: Managing stream partitioning, offset tracking, consumer lag remediation, schema registry integration (Avro/JSON Schema), and message serialization/deserialization. Python: The foundational language used across custom automation scripts and microservices infrastructure. Code/Practices: Object-oriented programming, utilizing concurrency modules (asyncio, multiprocessing), building API integration logic, and strict custom unit-testing suites.

3. Big Data Transformation & Analytical Storage Snowflake: Core corporate multi-cluster data platform serving all downstream business analytics, data science, and operational visibility workloads. Code/Practices: Writing complex analytical Window functions, executing optimized micro-partitioning queries, dynamic data masking, managing virtual warehouse clustering, and building zero-copy clones. The default transformation layout to convert raw staging data into clean data warehouse structures. Code/Practices: Developing core modular SQL models, writing customized schema-testing configurations (data_tests), managing documentation builds, and structuring semantic dimensional tables (Star Schema/Data Vault 2.0). Apache Airflow / Dagster: Centralized DAG system orchestrating cross-system dependencies, sensor alerts, and workflow execution.

4. Observability & Data Infrastructure AutomationData Quality & Monitoring Suites: Tools (such as AWS CloudWatch, Datadog, Great Expectations, or Monte Carlo) used to drive end-to-end operational visibility and data pipeline monitoring. Code/Practices: Creating customized alerting metrics, synthetic anomaly detection triggers, tracking consumer behavior trends, and telemetry ingestion logs. Terraform: Primary declarative Infrastructure-as-Code (IaC) tool for managing cloud state and deploying data architecture. Code/Practices: Writing modular HCL (HashiCorp Configuration Language) manifests to scale Amazon RDS instances, secure IAM roles, and spin up storage policies. Foundation of automated delivery systems.Code/Practices: Designing semantic release pipelines, multi-environment infrastructure tracking (dev, staging, prod), and formatting custom linting checkpoints.

Skills

Python, Aws, snowflake, airflow, rags, mlops, aws bedrock, dagster, Prefect, Immuta, Debezium, Golang, Scala

Top Skills Details

Python,Aws,snowflake,airflow,rags,mlops,aws bedrock

Additional Skills & Qualifications

Advanced Stack (Nice-to-Haves) Streaming Extensions: Debezium (Change Data Capture/CDC to pull changes from databases instantly). Performance Code: Go (Golang) or Scala (fast microservices for heavy traffic). Orchestration & AI: Dagster or Prefect (modern pipeline workflows), MLflow or Feast (MLOps feature stores). Governance & Testing: Immuta (PII data masking), Great Expectations or Soda

Experience Level

Expert Level

Job Type & Location

This is a Permanent position based out of Addison, TX.

Pay and Benefits

The pay range for this position is $175000.00 - $200000.00/yr.

Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.

FLAVOR PERKS: Unlimited paid time off for exempt employees One paid volunteer day of your choice Competitive bonus structure for eligible roles Team member stock purchase plan Health savings or flexible spending account options 401k (dollar for dollar on the first 3% and then 50 cents on the dollar for the next 2% for team member contributions up to 5% of eligible compensation) Comprehensive medical, dental, and vision benefits Basic life and AD&D insurance provided Pet insurance Education Assistance Wellness reimbursement program Paid maternity and paternity leave

Job Type & Location

This is a Permanent position based out of Addison, TX.

Application Deadline

This position is anticipated to close on Nov 16, 2026.

Workplace Type

This is a fully onsite position in Addison,TX.

The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.

San Francisco Fair Chance Ordinance: Pursuant to the San Francisco Fair Chance Ordinance, for all positions located in the city and county of San Francisco, we will consider for employment qualified applicants with arrest and conviction records.

Massachusetts Lie Detector: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.