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Key Responsibilities and Required Skills for Big Data Engineer

💰 $ - $

EngineeringDataBig Data

🎯 Role Definition

A Big Data Engineer designs, builds, and operates scalable data platforms and pipelines that enable analytics, ML, and business intelligence at scale. This role focuses on architecting and implementing batch and stream processing solutions using Hadoop ecosystem tools, Apache Spark, Kafka, cloud-native services (AWS/GCP/Azure), and data lakehouse patterns. The Big Data Engineer partners with data scientists, analysts, and product teams to deliver reliable, high-performance data products, ensure data quality, and maintain secure, governed data environments.

Key SEO terms: Big Data Engineer, data engineer, data pipeline, Apache Spark, Kafka, Hadoop, AWS, GCP, Azure, data lake, ETL, stream processing, batch processing, data platform, data architecture.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Engineer
  • Software Engineer with data-focused experience
  • ETL Developer or Data Analyst transitioning to engineering

Advancement To:

  • Senior Big Data Engineer
  • Lead Data Engineer / Data Platform Lead
  • Data Engineering Manager or Head of Data Infrastructure
  • Principal Data Engineer / Architect

Lateral Moves:

  • Machine Learning Engineer
  • Data Architect
  • Analytics Engineer / BI Engineering Lead

Core Responsibilities

Primary Functions

  • Architect, design, and implement scalable, robust batch and streaming data pipelines using Apache Spark, Spark Streaming/Structured Streaming, Apache Flink or similar frameworks to ingest, transform, and store terabytes to petabytes of data for analytics and ML consumption.
  • Build and maintain resilient data ingestion pipelines from diverse sources (RDBMS, NoSQL, event streams, APIs, logs) using Kafka, Kinesis, Pub/Sub, NiFi, or custom connectors, ensuring high throughput and low latency.
  • Design and manage data storage solutions including data lakes (S3, GCS, ADLS), data warehouses (Redshift, Snowflake, BigQuery), and lakehouse architectures with file formats like Parquet, ORC, and Delta Lake to optimize query performance and storage costs.
  • Implement ETL/ELT processes, transforming raw data into curated, analytics-ready datasets with reproducible, tested, and documented pipelines (Airflow, Prefect, Luigi, Dagster).
  • Optimize Spark jobs, SQL queries, and cluster configurations for performance and cost efficiency, including tuning partitioning, caching, memory settings, and shuffle behavior.
  • Develop and operate scalable cluster environments using Hadoop YARN, EMR, Dataproc, EKS, or managed serverless compute, including provisioning, autoscaling, monitoring, and cost governance.
  • Design and enforce data schema, metadata management, and cataloging with tools like Hive Metastore, AWS Glue Catalog, Data Catalog, Amundsen, or Apache Atlas to ensure discoverability and lineage.
  • Implement streaming architectures and real-time processing patterns (exactly-once processing, windowing, watermarking) to support use cases such as fraud detection, personalization, and operational metrics.
  • Integrate and manage message queues and event streaming platforms (Kafka, Confluent Platform, Pulsar) including topic design, retention policies, schema registry (Avro/Protobuf), and consumer group tuning.
  • Define and implement data quality frameworks and automated validation tests, using Great Expectations, Deequ, or custom checks, to monitor pipeline health and detect anomalies.
  • Collaborate closely with data scientists to productionize ML feature stores, pipelines, and model inference services, ensuring reproducible feature engineering and reliable feature delivery.
  • Implement CI/CD for data engineering code and infrastructure-as-code (Terraform, CloudFormation, Pulumi), enabling automated testing, deployment, and rollbacks of pipelines and clusters.
  • Establish robust observability and monitoring for data platforms using Prometheus, Grafana, CloudWatch, Stackdriver, Datadog, or ELK stack to detect failures, measure SLAs, and alert on data pipeline degradation.
  • Enforce security best practices for data at rest and in transit, including IAM policies, encryption, VPC configurations, network controls, and role-based access to sensitive datasets.
  • Collaborate with product managers, analytics, and business stakeholders to translate business requirements into scalable data solutions and clearly scoped data products with SLAs.
  • Lead root cause analysis and incident response for production data failures, post-mortems, and continuous improvement to reduce mean time to recovery (MTTR) and recurrence.
  • Migrate and modernize legacy ETL workloads to cloud-native data platforms and managed services, ensuring backward compatibility, performance parity, and cost optimization.
  • Design partitioning, compaction, and lifecycle policies for data retention, archival, and tiering to balance query performance with storage cost.
  • Implement data governance programs, including lineage tracking, classification, and GDPR/CCPA compliance measures to ensure privacy and regulatory adherence.
  • Create clear technical documentation, runbooks, and onboarding guides for data platform usage, coding standards, and pipeline operational procedures.
  • Mentor junior engineers, run code reviews, and contribute to hiring and technical recruiting to grow a high-performing data engineering team.
  • Prototype and evaluate new big data technologies, open-source tools, and managed cloud services to continuously improve platform capabilities and developer productivity.
  • Collaborate with DevOps and infrastructure teams to provide production-grade deployment pipelines, secret management, and standardized container images for data workloads.
  • Implement cost monitoring and chargeback mechanisms to attribute data platform usage to business units and drive economic efficiency across data operations.

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.

Required Skills & Competencies

Hard Skills (Technical)

  • Apache Spark: advanced experience developing, tuning, and deploying Spark jobs (Scala, PySpark) for large-scale batch and streaming workloads.
  • Distributed data processing: deep knowledge of Hadoop ecosystem (HDFS, YARN), Flink, or other distributed compute engines and parallel processing patterns.
  • Messaging & streaming: practical experience with Apache Kafka, Kinesis, or Pub/Sub for high-throughput event-driven architectures, including schema registry and exactly-once semantics.
  • Cloud data platforms: hands-on experience with AWS (EMR, Glue, Redshift, S3), GCP (Dataproc, Dataflow, BigQuery, GCS), or Azure (HDInsight, Synapse, ADLS).
  • Data modeling & ETL/ELT: strong understanding of dimensional modeling, schema design, incremental loads, CDC (Debezium), and ELT patterns for analytics and reporting.
  • Data storage formats & lakehouse tech: familiarity with Parquet/ORC, Delta Lake, Iceberg, Hudi, and best practices for partitioning and compaction.
  • Orchestration & workflow tools: expertise with Airflow, Prefect, Dagster, or similar orchestration frameworks for scheduling and dependency management.
  • SQL & performance tuning: expert-level SQL skills for complex queries, performance profiling, and optimization on distributed warehouses and engines.
  • Infrastructure as Code & CI/CD: experience implementing Terraform, CloudFormation, GitOps, and automated testing/deployment pipelines for data code and infra.
  • Monitoring & observability: experience configuring Prometheus/Grafana, CloudWatch, Datadog, or ELK for pipeline metrics, logs, and alerting.
  • Programming languages: proficient in Python, Scala, and/or Java for data processing, automation, and tooling development.
  • Data governance & security: knowledge of IAM, encryption, auditing, data masking, PII handling, and regulatory compliance frameworks.
  • Containerization & orchestration: experience with Docker and Kubernetes (EKS/GKE/AKS) for deploying scalable data services and microservices.
  • Performance & cost optimization: demonstrated ability to profile workloads, optimize compute/storage usage, and implement cost-saving measures.

Soft Skills

  • Strong problem-solving: ability to diagnose complex distributed system issues and design pragmatic, scalable solutions.
  • Communication: clear written and verbal communication skills for cross-functional collaboration with analytics, product, and business stakeholders.
  • Collaboration: experience working in agile teams, facilitating technical discussions, and aligning stakeholders on priorities and trade-offs.
  • Ownership & accountability: proven track record of taking end-to-end responsibility for production data systems and delivery.
  • Mentorship: ability to coach junior engineers, give actionable code reviews, and grow team capabilities.
  • Business acumen: understands business metrics and translates technical work into measurable business outcomes.
  • Adaptability: comfortable learning and evaluating new technologies, frameworks, and evolving architectural patterns.
  • Time management: strong prioritization skills to balance engineering debt, new feature delivery, and operational responsibilities.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Computer Science, Data Science, Machine Learning, or a related technical discipline (preferred but not required).

Relevant Fields of Study:

  • Computer Science
  • Data Science
  • Software Engineering
  • Information Systems
  • Statistics / Applied Mathematics
  • Electrical or Computer Engineering

Experience Requirements

Typical Experience Range:

  • 3–8 years of professional experience in data engineering, big data, or backend engineering roles; mid-level often 3–5 years, senior 5+ years.

Preferred:

  • 5+ years building and operating large-scale batch and streaming data pipelines in production.
  • Demonstrated experience with cloud-native data platforms (AWS/GCP/Azure), Apache Spark, Kafka, and data lake/warehouse architectures.
  • Prior experience with data governance, data quality frameworks, and implementing observability for data systems.
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