Data Engineering Manager, AI & Data Modernization

Accenture

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Chicago, ILMilwaukee, WIDallas, TXColumbus, OHKirkland, WACincinnati, OHNew York, NYCleveland, OHOklahoma City, OKAustin, TXAlbany, NYSt. Pete, FLHartford, CTPittsburgh, PASt. Louis, MOMiami, FLSacramento, CARaleigh, NCMinneapolis, MNMountain View, CAScottsdale, AZSan Francisco, CAMorristown, NJDenver, COBoston, MAPhiladelphia, PADes Moines, IAOverland Park, KSLos Angeles, CACharlotte, NCWalnut Creek, CACarmel, CASeattle, WAHouston, TXArlington, TXAtlanta, GAOrlando, FLRedmond, WABentonville, ARBeaverton, ORNashville, TNDetroit, MISan Diego, CA
Salary
$94,400–$293,800 / yr
Employment
Full-time
Posted
3 days ago
Freshness
Confirmed live today

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $181k
This role $194k
$70k $318k
below market most similar roles pay here above market

This role pays more than 56% of similar roles. Most pay $140,000–$222,033 — the blue band above. At the midpoint, this role pays about $194k versus about $181k for comparable roles.

Based on 240 similar postings.

Employer

About Accenture

Accenture is a leading global professional services company specializing in IT, strategy, consulting, and operations, with a strong focus on digital transformation, cloud computing, and artificial intelligence.

Accenture currently has 219 open roles on FindRole.

Listed pay typically runs $94,400–$266,300 across 160 roles with salary data.

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At a glance

TL;DR · Data Engineering Manager, AI & Data Modernization

The Data Engineering Manager – AI & Data Modernization leads data engineering work on modernization engagements within the Digital Core Reinvention practice. The role involves building batch and streaming pipelines, productizing data for AI consumption, and establishing data-plane governance, including cataloging, quality, lineage, and observability. The successful candidate will design foundations for both AI workloads and classical analytics. Required technical skills include Python, SQL, and experience with at least one cloud data platform, streaming technologies like Kafka, and orchestration tools such as Airflow. Candidates should possess hands-on experience with LLM-based systems and production-scale engineering. The work focuses on creating resilient, trusted data foundations and lakehouse substrates to power AI at scale, ensuring that data flowing into models and agents is reliable and governed.

What you'll do

  • Lead data engineering work for modernization engagements including batch and streaming pipelines.
  • Develop productized data specifically ready for AI consumption.
  • Implement data-plane governance including cataloging, quality, lineage, and observability.
  • Build resilient and trusted data foundations to power AI at scale.
  • Design data foundations for both AI workloads and classical analytics.
  • Manage production-scale data pipelines using Python, SQL, and cloud platforms.
  • Utilize streaming technologies and orchestration tools like Kafka or Airflow.

What we're looking for

  • Minimum of 5 years of hands-on data engineering or data platform engineering experience.
  • Minimum of 1–2 years of experience specifically on AI/LLM-related data work.
  • Minimum of 5 years of experience working in agile engineering or consulting teams.
  • Minimum of 5 years of experience with Python and SQL at production scale.
  • Minimum of 5 years of experience with streaming technologies and orchestration tooling like Kafka or Airflow.
  • Minimum of 1 year of experience designing for both AI workloads and classical analytics on the same foundation.
  • Bachelor's degree, or an Associate's degree with 6 years of experience, or 12 years of work experience.
  • Experience with Lakehouse/open-table formats, data-mesh, or vector and graph stores (preferred).

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