Key Responsibilities and Required Skills for Data Engineering Manager

🎯 Role Definition

The Data Engineering Manager leads a team of engineers responsible for building and operating the organization's data platform, data pipelines, and ingestion/processing systems that power analytics and machine learning. This role translates business requirements into technical designs, sets engineering standards for data quality and observability, partners with analytics, product and ML teams, and drives a roadmap for scalable, cost-effective, and secure data solutions across cloud and hybrid environments.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Data Engineer
Big Data Engineer / Platform Engineer
Analytics Engineering Lead

Advancement To:

Head of Data Engineering / Director of Data Engineering
Head of Data Platforms / VP of Data
Chief Data Officer (CDO) or Senior Technical Leader for Data & AI

Lateral Moves:

Machine Learning Engineering Manager
Analytics/BI Engineering Lead
Platform Engineering Manager (Infrastructure/DevOps focus)

Core Responsibilities

Primary Functions

Lead the design, development, deployment, and operation of robust, scalable data pipelines (batch and streaming) that ingest, transform, and deliver data for analytics, reporting and machine learning, using technologies such as Apache Spark, Kafka, Flink, Airflow/Orchestration, and cloud data services.
Define and own the data platform roadmap, prioritizing initiatives that reduce technical debt, increase data product velocity, and deliver measurable business outcomes across marketing, finance, product and data science stakeholders.
Architect modern data warehouses and lakehouse solutions (Snowflake, BigQuery, Redshift, Databricks, Delta Lake) with a focus on performance, cost optimization, partitioning strategies, and schema design to support high-concurrency analytics.
Build and operationalize CI/CD pipelines for data infrastructure and transformation logic (dbt, Terraform, GitOps) to enable repeatable deployments, testing and rollback of data assets.
Implement data ingestion solutions from transactional systems, third-party APIs, streaming platforms and event sources; design retry strategies, schema evolution handling, and backfill patterns.
Establish and enforce data quality, validation, and monitoring frameworks (unit tests, data contracts, data quality checks, SLOs) to detect regressions, drift, and anomalies before they impact consumers.
Drive observability and incident response for data pipelines and services using logging, metrics, tracing and alerting (Prometheus, Grafana, Datadog, Sentry), and lead post-incident reviews and continuous improvement actions.
Manage, mentor and grow a high-performing team of data engineers and analytics engineers through coaching, 1:1s, performance reviews, career pathing, and structured hiring to scale the organization.
Collaborate with product managers, analytics, ML teams and business stakeholders to translate use cases into measurable success criteria and prioritized engineering work streams.
Define and maintain data governance practices including lineage, metadata management, access controls, encryption, and compliance with regulatory requirements (GDPR, CCPA) in partnership with security and legal teams.
Drive standardization of schemas, naming conventions, and transformation patterns across teams to reduce duplication, improve discoverability, and increase data reuse.
Evaluate, select and integrate third-party and open-source tools and managed services to accelerate delivery while keeping total cost of ownership and vendor lock-in in check.
Create and enforce best practices for code quality, modular transformation patterns, performance testing, and capacity planning to support scale and predictability.
Partner with cloud engineering to design secure, cost-effective infrastructure for data processing workloads (serverless, Kubernetes, managed Spark clusters), including selection of instance types and autoscaling policies.
Sponsor a data product mindset: define SLAs, onboarding flows, documentation and service-level agreements for internal consumers and product teams that rely on data.
Quantify business impact—track adoption, latency improvements, cost savings, query performance gains, and other KPIs to justify investments and guide roadmap decisions.
Lead cross-functional programs to consolidate data sources, retire legacy ETL pipelines, and migrate to centralized, governed data platforms while minimizing business disruption.
Negotiate and prioritize technical debt reduction and platform enhancements with stakeholders; balance feature delivery and platform reliability.
Drive data security posture through role-based access policies, least privilege models, key management, and automated provisioning workflows for datasets.
Advocate for and implement reproducible data transformation development via unit testing, data mocks, and integration testing to reduce regression risk in production deployments.
Plan and execute capacity and disaster recovery strategies for mission-critical data services, including backups, point-in-time recovery, and multi-region replication where applicable.
Mentor engineers in performance tuning and query optimization (SQL, Spark jobs), and establish guidelines for cost-aware coding and storage optimization.
Represent the data engineering organization in executive reviews, roadmap presentations and partner with finance on cost forecasting and chargeback models.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Run workshops and brown-bags to raise data literacy across the company and onboard new data consumers.
Maintain up-to-date documentation, onboarding guides, data catalogs and runbooks for platform users.
Evaluate and pilot emerging data technologies and patterns (e.g., lakehouse architectures, vector databases for ML, feature stores).
Assist in vendor evaluations, contract negotiations and proof-of-concepts for data platform purchases.

Required Skills & Competencies

Hard Skills (Technical)

Deep expertise in SQL and performance tuning for analytical workloads (complex joins, window functions, indexing, partitioning).
Strong programming experience in Python (pandas, PySpark), Scala or Java for ETL/streaming and transformation development.
Experience building and operating large-scale distributed data processing using Apache Spark, Flink or equivalent frameworks.
Proficiency with cloud data platforms and services: AWS (Redshift, Glue, EMR, S3), GCP (BigQuery, Dataflow), or Azure (Synapse, Databricks).
Hands-on experience with cloud-native data warehouse and lakehouse technologies (Snowflake, Databricks, BigQuery, Delta Lake).
Familiarity with orchestration tools and workflow schedulers (Airflow, Prefect, Dagster) and building pipelines with CI/CD.
Knowledge of streaming technologies (Kafka, Kinesis, Pub/Sub) and event-driven architectures for real-time data flows.
Practical experience with infrastructure-as-code and provisioning tools (Terraform, CloudFormation) and containerization (Docker, Kubernetes).
Expertise in data modeling, ETL/ELT patterns, slowly changing dimensions, schema evolution and data partitioning strategies.
Experience implementing data quality, observability and monitoring patterns (Great Expectations, Monte Carlo, OpenTelemetry).
Understanding of data governance, metadata management, data lineage and access control systems (Collibra, Alation, Apache Atlas).
Familiarity with analytics engineering tools like dbt and practices for modular, tested transformations.
Knowledge of cost optimization techniques for cloud data storage and compute, and experience with budget/chargeback models.

Soft Skills

Strong leadership and people-management skills: coaching, delegation, recruitment, performance feedback and career development.
Excellent stakeholder management and cross-functional collaboration; ability to synthesize business needs into technical roadmaps.
Product mindset with strong prioritization skills and ability to measure impact and outcomes.
Clear communicator who can present technical concepts to non-technical audiences and influence senior leaders.
Problem-solving orientation with strong debugging and root-cause analysis skills in production systems.
Change management and program delivery experience, including managing dependencies across teams.
Empathy and mentorship to grow team capability and promote psychological safety.
Agile practitioner able to run iterative delivery, manage scope and avoid over-commitment.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Software Engineering, Information Systems, Mathematics, Statistics, or related technical discipline.

Preferred Education:

Master’s degree in Computer Science, Data Science, Business Analytics, or equivalent advanced degree.
Certifications in cloud platforms (AWS/GCP/Azure) or data engineering (e.g., Databricks, Snowflake) are a plus.

Relevant Fields of Study:

Computer Science
Data Science
Software Engineering
Information Systems
Mathematics / Statistics
Applied Engineering / Cloud Computing

Experience Requirements

Typical Experience Range:

6–12+ years of professional experience in data engineering, software engineering, or platform engineering, with at least 2–5 years in a managerial or tech-lead role.

Preferred:

8+ years building data platforms and pipelines in production and 3+ years managing and scaling engineering teams.
Track record shipping data products used by cross-functional teams and experience in cloud migrations, cost optimization, or centralizing disparate data estates.