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Key Responsibilities and Required Skills for Data Support Engineer

💰 $80,000 - $130,000

DataEngineeringSupportAnalyticsCloud

🎯 Role Definition

The Data Support Engineer is a hybrid technical-support and data-engineering role focused on ensuring reliable delivery of production data, diagnosing and resolving incidents in ETL/data pipeline systems, onboarding and empowering analytics consumers, and driving continuous improvements in data quality and operational observability. This role sits at the intersection of data engineering, incident response, and customer-facing analytics enablement — accelerating insights by keeping critical data systems healthy and trusted.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to operational support for production data systems.
  • Junior Data Engineer or ETL Developer with experience supporting pipelines.
  • Technical Support Engineer or SRE with experience in databases and analytics tooling.

Advancement To:

  • Senior Data Support Engineer / Staff Data Engineer focusing on platform reliability.
  • Data Engineering Team Lead or Manager overseeing pipeline reliability and operations.
  • Analytics Engineering Manager or Data Platform Product Manager.

Lateral Moves:

  • Analytics Engineer (dbt-focused analytics and modeling).
  • Site Reliability Engineer (SRE) for data/platform services.
  • BI Developer or Solutions Engineer supporting analytics adoption.

Core Responsibilities

Primary Functions

  • Act as the first-line responder for production data incidents: triage alerts from monitoring systems (Airflow, dbt, Snowflake, etc.), diagnose root causes across ETL jobs, APIs, and data warehouses, and coordinate timely remediation with engineering teams and stakeholders.
  • Troubleshoot complex SQL, Python, or Spark job failures end-to-end, including analyzing logs, query plans, resource contention, and dependency graphs to restore data flows and prevent downstream impact to dashboards and analytics.
  • Maintain and operate data pipeline orchestration (Airflow, Prefect, Dagster), including creating and modifying DAGs, defining retries and SLAs, and improving observability through task-level metrics and logs.
  • Ensure data quality and trust by implementing, running, and refining automated data quality checks (Great Expectations, dbt tests, custom checks), investigating anomalies, and documenting corrective actions and preventive measures.
  • Manage and optimize cloud data warehouse objects and performance (Snowflake, BigQuery, Redshift) by analyzing slow queries, tuning warehouse sizing, partitioning/clustering strategies, and advising on cost-effective storage and compute patterns.
  • Support onboarding and enablement for analytics consumers by building and maintaining clear runbooks, FAQs, and self-serve troubleshooting guides for common pipeline issues, schema changes, and access procedures.
  • Monitor and maintain real-time and streaming data infrastructure (Kafka, Kinesis, Pub/Sub) health, process lag, consumer group statuses, and handle backpressure or schema evolution issues that affect downstream analytics.
  • Serve as the liaison between business stakeholders, analytics teams, and platform engineering to translate business data requirements into operational priorities and changes in pipeline scheduling, SLA definitions, and data contract agreements.
  • Execute and automate incident postmortems and RCA (root cause analysis) for critical production outages, documenting findings, impact assessments, and tracking remediation and preventive action items through to closure.
  • Implement and maintain data ingestion frameworks and connectors (REST APIs, JDBC, S3, GCS), resolving authentication, rate-limit, and schema drift issues while ensuring safe retries and idempotency in ingestion logic.
  • Build and maintain monitoring dashboards (Grafana, Looker, Tableau) and alerting rules for pipeline latency, success rates, and data freshness, continually iterating to reduce noise and surface actionable events.
  • Manage schema evolution across producers and consumers, including coordinating schema migrations, maintaining backward/forward compatibility, and updating contracts to prevent pipeline breaks and silent data corruption.
  • Perform proactive housekeeping and operational tasks such as vacuuming, compaction, partition maintenance, and retention policies to preserve query performance and warehouse cost-efficiency.
  • Work with the data engineering team to design and implement scalable ETL/ELT best practices (modular pipelines, incremental loads, CDC patterns), advocating for testability, observability, and recoverability in pipeline design.
  • Support data access governance by implementing role-based access controls, auditing data usage incidents, and collaborating with security and compliance teams to respond to data incident inquiries.
  • Automate repetitive support tasks using scripts and small tools (Bash, Python) to reduce mean time to recovery (MTTR), including automated restarts, dependency checks, and bulk repair jobs.
  • Validate and reconcile production data with upstream sources during onboarding or schema changes, ensuring end-to-end data lineage and measurable reconciliation reports.
  • Participate in capacity planning and cost optimization for data infrastructure, providing estimates and recommendations for scaling compute, storage, and streaming resources based on SLA and usage patterns.
  • Collaborate closely with analytics engineers and BI teams to prioritize fixes that unblock reporting and iterative analysis, including fast-tracking critical bugfixes and hotpatches with clear risk assessment and rollback plans.
  • Maintain CI/CD pipelines for data code (Airflow DAGs, dbt, SQL-based transformations), enforce code review and testing standards, and support versioned deployment strategies for safe rollouts.
  • Educate non-technical stakeholders on data lifecycle, common failure modes, and best practices for reducing breakage risk, acting as a trusted advisor for data consumers and product teams.

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)

  • Expert SQL skills for troubleshooting, performance tuning, and complex ad-hoc analytics across large datasets.
  • Strong Python experience for scripting, automation, job orchestration, and writing recovery/repair utilities.
  • Production experience with cloud data warehouses and query engines (Snowflake, BigQuery, Amazon Redshift) and understanding of cost/performance tradeoffs.
  • Hands-on familiarity with ETL/ELT tooling and patterns (dbt, Airflow, Prefect, Dagster, Matillion) and the ability to debug orchestration failures.
  • Experience with streaming platforms and message brokers (Kafka, Kinesis, Pub/Sub) and with diagnosing consumer lag, offset handling, and schema compatibilities.
  • Proficiency with data quality frameworks and validation tools (Great Expectations, dbt tests, custom checks) and designing coverage for critical datasets.
  • Monitoring and observability tooling experience (Grafana, Prometheus, Sentry, DataDog) to build alerts and dashboards that reduce operational MTTR.
  • Knowledge of data formats and serialization (Parquet, Avro, ORC, JSON) and best practices for partitioning, compression, and schema evolution.
  • Familiarity with cloud platforms and infrastructure-as-code (AWS/GCP/Azure, Terraform) for diagnosing permission, network, and resource provisioning issues.
  • Version control and CI/CD experience (Git, GitHub Actions, Jenkins, GitLab CI) to support safe deployments of data code and pipeline changes.
  • Experience with database internals and performance (indexes, partitioning, vacuuming, query plans) for both OLAP and OLTP systems.
  • REST APIs, authentication mechanisms (OAuth, API keys), and data ingestion patterns to troubleshoot external data sources.
  • Basic knowledge of containerization and orchestration (Docker, Kubernetes) when data services are deployed in containerized environments.
  • Ability to read and interpret system and job logs, stack traces, and monitoring metrics to trace multi-component failure modes.

Soft Skills

  • Strong analytical and problem-solving aptitude with a bias for root-cause analysis and durable fixes rather than temporary workarounds.
  • Customer-focused mindset: ability to communicate technical issues clearly to non-technical stakeholders and set realistic expectations under incident load.
  • Excellent written communication for authoring runbooks, postmortems, and documentation that scale across teams and time zones.
  • Collaborative team player able to convene cross-functional stakeholders and drive issues to resolution with diplomacy and urgency.
  • Time management and prioritization skills under pressure, balancing incident response with engineering improvements and technical debt reduction.
  • Continuous improvement mindset: comfortable automating manual tasks and advocating for operational excellence and documentation.
  • High attention to detail for detecting subtle data regressions and ensuring data correctness for downstream analytics.
  • Adaptability to changing tech stacks and willingness to learn new data platforms, tools, and observability practices.
  • Proactive ownership and accountability for SLAs, operational metrics, and the end-to-end reliability of data services.
  • Coaching and mentoring: ability to onboard junior engineers and enable analytics consumers to be more self-sufficient.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, Mathematics, Statistics, or a related technical discipline — or equivalent practical experience.

Preferred Education:

  • Bachelor's or Master's degree in Computer Science, Data Engineering, or related field with coursework in databases, distributed systems, and software engineering.
  • Certifications in cloud platforms (AWS/GCP/Azure) or data tooling (Snowflake, dbt) are a plus.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Software Engineering
  • Mathematics / Statistics

Experience Requirements

Typical Experience Range: 2–6 years in data engineering, analytics engineering, or data operations roles with hands-on support of production data systems.

Preferred:

  • 3+ years supporting production ETL/ELT pipelines, cloud data warehouses, and data orchestration tooling.
  • Demonstrable experience resolving high-severity incidents, performing postmortems, and implementing preventive controls.
  • Experience working in cross-functional environments supporting analytics consumers and product teams.
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