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About This Role
At TTEC Digital, we coach clients to ensure their employees feel valued, and fully supported, because an amazing customer experience is an employee first process. Our vision is the same, a place where employees know they can thrive.
The TTEC Digital Microsoft Partner Practice, consisting of over 100 experts globally and growing, have the most recent certifications and experience with Azure AI, Power Platform and Microsoft Business Applications, earning them AI Inner Circle Awards, Eagle Awards, Partner of the Year, and eleven Inner Circle awards, all of which validate our employees' innovative industry solutions that fuel our clients' exceptional customer experience (CX).
We are seeking an experienced Senior Data Engineer to lead the design and delivery of end-to-end data platforms for our clients across the Microsoft Azure Data & AI stack (Azure Data Factory, Microsoft Fabric, Synapse, ADLS Gen2) and Azure Databricks.
This is a senior, hands-on technical role: you will own architecture and data modeling decisions, build production-grade pipelines yourself, and mentor other engineers, while also acting as a credible technical voice with client stakeholders.
The ideal candidate has deep experience delivering the full data lifecycle - ingestion, cleaning, preparation, analytics, and activation - and is comfortable designing the APIs and Model Context Protocol (MCP) integrations that let front-line systems and Data Agents / agentic AI interfaces consume that data.
What You'll Be Doing
End-to-End Platform Architecture
- Lead the design and build of end-to-end data platforms spanning ingestion, cleaning, preparation, transformation, analytics, and activation, using Azure Data Factory, Microsoft Fabric (Lakehouse, Warehouse, OneLake), and/or Azure Databricks with PySpark, Python, and SQL.
- Architect Medallion (Bronze, Silver, Gold) lakehouse patterns using OneLake/Delta Lake, balancing performance, reliability, and cost.
- Own data modeling for client engagements - dimensional, normalized, and semantic layers - so that data is trustworthy, reusable, and analytics-ready across domains.
- Design batch and streaming ingestion patterns, including Fabric Data Factory/pipelines, Azure Databricks Structured Streaming, and event-driven architectures (e.g., Event Hubs).
- Identify and resolve architectural risk and technical debt, and optimize pipelines for performance and cost.
APIs, Data Agents & Activation
- Design and build APIs and data services that activate governed data for client analytics, reporting, and downstream applications.
- Design MCP (Model Context Protocol) and API-based integrations that let front-line business systems, Fabric/Copilot Data Agents, and other agentic AI interfaces securely query and exchange data with the platform.
- Partner with AI/Architecture teams to expose enterprise data to Data Agents and Generative AI applications (e.g., Fabric Data Agents, Azure AI Foundry) in a governed, reusable way.
- Implement data governance patterns using Microsoft Purview and/or Unity Catalog, including access control, metadata, and lineage.
- Support production operations and incident response for critical pipelines and activation services on client engagements.
Technical Leadership & Client Engagement
- Mentor and provide technical direction to Data Engineers, reviewing code, data models, and designs to raise delivery quality.
- Act as a trusted technical resource to client stakeholders, explaining architecture trade-offs and design decisions in clear terms.
- Contribute to reusable frameworks, accelerators, and engineering standards used across engagements.
- Support presales activities where needed, including technical input on estimates, proposals, and solution design.
- Evaluate new capabilities across Fabric and Azure Databricks and recommend where they add value for client platforms.
What You'll Bring to the Role
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical discipline.
- 5-8 years of experience in data engineering, with strong ownership of production data pipelines on Azure.
- Expert-level proficiency in Python, SQL, and PySpark for building distributed data pipelines at scale.
- Deep hands-on experience with the Microsoft Azure Data & AI stack (Azure Data Factory, Microsoft Fabric, Synapse, ADLS Gen2) and/or Azure Databricks, including Delta Lake/OneLake and Medallion architecture patterns.
- Strong background in data modeling, schema design, and enterprise data architecture across the full lifecycle from ingestion to activation.
- Experience designing APIs and MCP-based integrations that exchange data with front-line systems, Data Agents, and other agentic AI interfaces.
- Experience with orchestration and CI/CD (Fabric pipelines, Azure Data Factory, Databricks Workflows, Azure DevOps).
- Strong knowledge of data governance and security tooling, including Microsoft Purview and/or Unity Catalog.
- Demonstrated ability to mentor engineers and lead technical design discussions on client-facing engagements.
- Strong problem-solving skills and ability to troubleshoot complex, large-scale data issues independently.
- Excellent communication skills, with the ability to explain technical trade-offs to both technical and client audiences.
- Relevant Microsoft certifications (e.g., Azure Data Engineer Associate, Fabric Analytics Engineer Associate) and/or Databricks certifications preferred.
#LI-DD1 #LI-Remote
Team Overview For eleven years in a row our Microsoft practice has achieved Microsoft's Inner Circle Award designation and we have been recognized as 'Partner of the year for Microsoft Dynamics 365 Service', ‘Partner of the year for Microsoft Dynamics 365 Marketing’ and as a Partner of the year finalist for ‘Intelligent Sales & Marketing’. In addition to our ongoing Gold Partner status, our competencies range from software development and deployment of Dynamics 365, to Power Platform, Azure and Microsoft 365. In mid-2022, the team was also named as one of only a handful of launch partners for the Microsoft D365 Contact Center. Our integrated team approach of experienced CX engineers, developers, data scientists, and solution architects, design, deploy and support complex and fully integrated digital solutions that are focused on creating an exceptional customer experience.
About Us Avtex is now TTEC Digital. The acquisition aligns to TTEC’s continued focus on fueling experience transformations. Our deep expertise in the most innovative and disruptive brands in the digital industry enables our clients to provide amazing customer experiences.
TTEC Digital, and our 1,700+ employees, pioneer engagement and growth solutions that fuel the exceptional customer experience (CX). TTEC Engage is a 60,000+ employee service company, with customers in more than 80 countries. Together, we utilize a holistic approach, applying solutions from two centers of excellence, Engage and Digital.
TTEC is a proud equal opportunity employer where all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability. TTEC has fully embraced and is committed to expanding our diverse and inclusive workforce. We strive to reflect the communities we serve while delivering amazing service and technology centered around humanity.
Rarely do applicants meet all desired job qualifications, so if you feel you would succeed in the role above, please take a moment and share your qualifications.
#LI-REMOTEUS
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $155K-$175K range is above the 75th percentile for Data Engineer roles in our dataset (median: $150K across 25 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 8,943 AI roles we're tracking, Data Engineer positions make up 1% of the market. At TTEC Digital, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,825 based on 160 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($165K) sits 7% below the category median. Disclosed range: $155K to $175K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
TTEC Digital AI Hiring
TTEC Digital has 1 open AI role right now. They're hiring across Data Engineer. Based in Austin, TX, US. Compensation range: $175K - $175K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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